sdi.models package

Submodules

sdi.models.aliases module

SDI - Semantic Data Interconnect APIs

The Semantic Data Interconnect (SDI) is a collection of APIs that allows the user to unlock the potential of disparate big data by connecting external data. The SDI can infer the schemas of data based on schema-on-read, allow creating a semantic model and perform big data semantic queries. It seamlessly connects to MindSphere’s Integrated Data Lake (IDL), but it can work independently as well. There are two mechanisms that can be used to upload files so that SDI can generate schemas and make data ready for query. The SDI operations are divided into the following groups: Data Registration for SDI This set of APIs is used to organize the incoming data. When configuring a Data Registry, you have the option to update your data based on a replace or append strategy. If you consider a use case where schema may change and incoming data files are completely different every time then replace is a good strategy. The replace strategy will replace the existing schema and data during each data ingest operation whereas the append strategy will update the existing schema and data during each data ingest operation. Custom Data Type for SDI The SDI by default identifies basic data types for each property, such as String, Integer, Float, Date, etc. The user can use this set of APIs to create their own custom data type. The SDI also provides an API under this category to suggest data type based on user-provided sample test values. Data Lake for SDI The SDI can process files uploaded provides endpoints to manage customer’s data lake registration based on tenant id, cloud provider and data lake type. The set of REST endpoint allows to create, update and retrieve base path for their data lake. The IDL customer needs to create an SDI folder that is under the root folder. Any file uploaded in this folder is automatically picked up by SDI to process via IDL notification. Data Ingest for SDI This set of APIs allows user to upload files, start an ingest job for uploaded files, find job status for ingested jobs or retrieve all job statuses. Schema Registry for SDI The SDI provides a way to find the generated schema in this category. Users can find an SDI generated schema for uploaded files based on source name, data tag or schema name. Data Query for SDI allows querying based on the extracted schemas. Important supported APIs are: * Query interface for querying semantically correlated and transformed data. * Stores and executes data queries. * Uses a semantic model to translate model-based query to physical queries. Semantic Model for SDI allows user to create semantic model ontologies based on the extracted one or more schemas. The important functionalities achieved with APIs are: * Contextual correlation of data from different systems. * Infers & Recommends mappings between different schemas. * Import and store Semantic model. # noqa: E501

class Aliases(attribute_name=None, alias_value=None)

Bases: object

Attributes:

attribute_types (dict): The key is attribute name

and the value is attribute type.

attribute_map (dict): The key is attribute name

and the value is json key in definition.

alias_value

Gets the alias_value of this Aliases.

Returns:The alias_value of this Aliases. Return type:str

attribute_map = {'alias_value': 'aliasValue', 'attribute_name': 'attributeName'}

attribute_name

Gets the attribute_name of this Aliases.

Returns:The attribute_name of this Aliases. Return type:str

attribute_types = {'alias_value': 'str', 'attribute_name': 'str'}

to_dict()

Returns the model properties as a dict

to_str()

Returns the string representation of the model

sdi.models.api_errors_view module

SDI - Semantic Data Interconnect APIs

The Semantic Data Interconnect (SDI) is a collection of APIs that allows the user to unlock the potential of disparate big data by connecting external data. The SDI can infer the schemas of data based on schema-on-read, allow creating a semantic model and perform big data semantic queries. It seamlessly connects to MindSphere’s Integrated Data Lake (IDL), but it can work independently as well. There are two mechanisms that can be used to upload files so that SDI can generate schemas and make data ready for query. The SDI operations are divided into the following groups: Data Registration for SDI This set of APIs is used to organize the incoming data. When configuring a Data Registry, you have the option to update your data based on a replace or append strategy. If you consider a use case where schema may change and incoming data files are completely different every time then replace is a good strategy. The replace strategy will replace the existing schema and data during each data ingest operation whereas the append strategy will update the existing schema and data during each data ingest operation. Custom Data Type for SDI The SDI by default identifies basic data types for each property, such as String, Integer, Float, Date, etc. The user can use this set of APIs to create their own custom data type. The SDI also provides an API under this category to suggest data type based on user-provided sample test values. Data Lake for SDI The SDI can process files uploaded provides endpoints to manage customer’s data lake registration based on tenant id, cloud provider and data lake type. The set of REST endpoint allows to create, update and retrieve base path for their data lake. The IDL customer needs to create an SDI folder that is under the root folder. Any file uploaded in this folder is automatically picked up by SDI to process via IDL notification. Data Ingest for SDI This set of APIs allows user to upload files, start an ingest job for uploaded files, find job status for ingested jobs or retrieve all job statuses. Schema Registry for SDI The SDI provides a way to find the generated schema in this category. Users can find an SDI generated schema for uploaded files based on source name, data tag or schema name. Data Query for SDI allows querying based on the extracted schemas. Important supported APIs are: * Query interface for querying semantically correlated and transformed data. * Stores and executes data queries. * Uses a semantic model to translate model-based query to physical queries. Semantic Model for SDI allows user to create semantic model ontologies based on the extracted one or more schemas. The important functionalities achieved with APIs are: * Contextual correlation of data from different systems. * Infers & Recommends mappings between different schemas. * Import and store Semantic model. # noqa: E501

class ApiErrorsView(errors=None)

Bases: object

Attributes:

attribute_types (dict): The key is attribute name

and the value is attribute type.

attribute_map (dict): The key is attribute name

and the value is json key in definition.

attribute_map = {'errors': 'errors'}

attribute_types = {'errors': 'list[ApiFieldError]'}

errors

Gets the errors of this ApiErrorsView.

Returns:The errors of this ApiErrorsView. Return type:list[ApiFieldError]

to_dict()

Returns the model properties as a dict

to_str()

Returns the string representation of the model

sdi.models.api_field_error module

SDI - Semantic Data Interconnect APIs

The Semantic Data Interconnect (SDI) is a collection of APIs that allows the user to unlock the potential of disparate big data by connecting external data. The SDI can infer the schemas of data based on schema-on-read, allow creating a semantic model and perform big data semantic queries. It seamlessly connects to MindSphere’s Integrated Data Lake (IDL), but it can work independently as well. There are two mechanisms that can be used to upload files so that SDI can generate schemas and make data ready for query. The SDI operations are divided into the following groups: Data Registration for SDI This set of APIs is used to organize the incoming data. When configuring a Data Registry, you have the option to update your data based on a replace or append strategy. If you consider a use case where schema may change and incoming data files are completely different every time then replace is a good strategy. The replace strategy will replace the existing schema and data during each data ingest operation whereas the append strategy will update the existing schema and data during each data ingest operation. Custom Data Type for SDI The SDI by default identifies basic data types for each property, such as String, Integer, Float, Date, etc. The user can use this set of APIs to create their own custom data type. The SDI also provides an API under this category to suggest data type based on user-provided sample test values. Data Lake for SDI The SDI can process files uploaded provides endpoints to manage customer’s data lake registration based on tenant id, cloud provider and data lake type. The set of REST endpoint allows to create, update and retrieve base path for their data lake. The IDL customer needs to create an SDI folder that is under the root folder. Any file uploaded in this folder is automatically picked up by SDI to process via IDL notification. Data Ingest for SDI This set of APIs allows user to upload files, start an ingest job for uploaded files, find job status for ingested jobs or retrieve all job statuses. Schema Registry for SDI The SDI provides a way to find the generated schema in this category. Users can find an SDI generated schema for uploaded files based on source name, data tag or schema name. Data Query for SDI allows querying based on the extracted schemas. Important supported APIs are: * Query interface for querying semantically correlated and transformed data. * Stores and executes data queries. * Uses a semantic model to translate model-based query to physical queries. Semantic Model for SDI allows user to create semantic model ontologies based on the extracted one or more schemas. The important functionalities achieved with APIs are: * Contextual correlation of data from different systems. * Infers & Recommends mappings between different schemas. * Import and store Semantic model. # noqa: E501

class ApiFieldError(code=None, logref=None, message=None, message_parameters=None)

Bases: object

Attributes:

attribute_types (dict): The key is attribute name

and the value is attribute type.

attribute_map (dict): The key is attribute name

and the value is json key in definition.

attribute_map = {'code': 'code', 'logref': 'logref', 'message': 'message', 'message_parameters': 'messageParameters'}

attribute_types = {'code': 'str', 'logref': 'str', 'message': 'str', 'message_parameters': 'list[MessageParameter]'}

code

Gets the code of this ApiFieldError.

Returns:The code of this ApiFieldError. Return type:str

logref

Gets the logref of this ApiFieldError.

Returns:The logref of this ApiFieldError. Return type:str

message

Gets the message of this ApiFieldError.

Returns:The message of this ApiFieldError. Return type:str

message_parameters

Gets the message_parameters of this ApiFieldError.

Returns:The message_parameters of this ApiFieldError. Return type:list[MessageParameter]

to_dict()

Returns the model properties as a dict

to_str()

Returns the string representation of the model

sdi.models.create_data_lake_request module

SDI - Semantic Data Interconnect APIs

The Semantic Data Interconnect (SDI) is a collection of APIs that allows the user to unlock the potential of disparate big data by connecting external data. The SDI can infer the schemas of data based on schema-on-read, allow creating a semantic model and perform big data semantic queries. It seamlessly connects to MindSphere’s Integrated Data Lake (IDL), but it can work independently as well. There are two mechanisms that can be used to upload files so that SDI can generate schemas and make data ready for query. The SDI operations are divided into the following groups: Data Registration for SDI This set of APIs is used to organize the incoming data. When configuring a Data Registry, you have the option to update your data based on a replace or append strategy. If you consider a use case where schema may change and incoming data files are completely different every time then replace is a good strategy. The replace strategy will replace the existing schema and data during each data ingest operation whereas the append strategy will update the existing schema and data during each data ingest operation. Custom Data Type for SDI The SDI by default identifies basic data types for each property, such as String, Integer, Float, Date, etc. The user can use this set of APIs to create their own custom data type. The SDI also provides an API under this category to suggest data type based on user-provided sample test values. Data Lake for SDI The SDI can process files uploaded provides endpoints to manage customer’s data lake registration based on tenant id, cloud provider and data lake type. The set of REST endpoint allows to create, update and retrieve base path for their data lake. The IDL customer needs to create an SDI folder that is under the root folder. Any file uploaded in this folder is automatically picked up by SDI to process via IDL notification. Data Ingest for SDI This set of APIs allows user to upload files, start an ingest job for uploaded files, find job status for ingested jobs or retrieve all job statuses. Schema Registry for SDI The SDI provides a way to find the generated schema in this category. Users can find an SDI generated schema for uploaded files based on source name, data tag or schema name. Data Query for SDI allows querying based on the extracted schemas. Important supported APIs are: * Query interface for querying semantically correlated and transformed data. * Stores and executes data queries. * Uses a semantic model to translate model-based query to physical queries. Semantic Model for SDI allows user to create semantic model ontologies based on the extracted one or more schemas. The important functionalities achieved with APIs are: * Contextual correlation of data from different systems. * Infers & Recommends mappings between different schemas. * Import and store Semantic model. # noqa: E501

class CreateDataLakeRequest(name=None, type=None, base_path=None)

Bases: object

Attributes:

attribute_types (dict): The key is attribute name

and the value is attribute type.

attribute_map (dict): The key is attribute name

and the value is json key in definition.

attribute_map = {'base_path': 'basePath', 'name': 'name', 'type': 'type'}

attribute_types = {'base_path': 'str', 'name': 'str', 'type': 'str'}

base_path

Gets the base_path of this CreateDataLakeRequest. This is currently supported only for the IDL customer. Please refer to the document section “For Integrated Data Lake (IDL) customers” on correct basePath structure.

Returns:The base_path of this CreateDataLakeRequest. Return type:str

name

Gets the name of this CreateDataLakeRequest.

Returns:The name of this CreateDataLakeRequest. Return type:str

to_dict()

Returns the model properties as a dict

to_str()

Returns the string representation of the model

type

Gets the type of this CreateDataLakeRequest.

Returns:The type of this CreateDataLakeRequest. Return type:str

sdi.models.create_data_lake_response module

SDI - Semantic Data Interconnect APIs

The Semantic Data Interconnect (SDI) is a collection of APIs that allows the user to unlock the potential of disparate big data by connecting external data. The SDI can infer the schemas of data based on schema-on-read, allow creating a semantic model and perform big data semantic queries. It seamlessly connects to MindSphere’s Integrated Data Lake (IDL), but it can work independently as well. There are two mechanisms that can be used to upload files so that SDI can generate schemas and make data ready for query. The SDI operations are divided into the following groups: Data Registration for SDI This set of APIs is used to organize the incoming data. When configuring a Data Registry, you have the option to update your data based on a replace or append strategy. If you consider a use case where schema may change and incoming data files are completely different every time then replace is a good strategy. The replace strategy will replace the existing schema and data during each data ingest operation whereas the append strategy will update the existing schema and data during each data ingest operation. Custom Data Type for SDI The SDI by default identifies basic data types for each property, such as String, Integer, Float, Date, etc. The user can use this set of APIs to create their own custom data type. The SDI also provides an API under this category to suggest data type based on user-provided sample test values. Data Lake for SDI The SDI can process files uploaded provides endpoints to manage customer’s data lake registration based on tenant id, cloud provider and data lake type. The set of REST endpoint allows to create, update and retrieve base path for their data lake. The IDL customer needs to create an SDI folder that is under the root folder. Any file uploaded in this folder is automatically picked up by SDI to process via IDL notification. Data Ingest for SDI This set of APIs allows user to upload files, start an ingest job for uploaded files, find job status for ingested jobs or retrieve all job statuses. Schema Registry for SDI The SDI provides a way to find the generated schema in this category. Users can find an SDI generated schema for uploaded files based on source name, data tag or schema name. Data Query for SDI allows querying based on the extracted schemas. Important supported APIs are: * Query interface for querying semantically correlated and transformed data. * Stores and executes data queries. * Uses a semantic model to translate model-based query to physical queries. Semantic Model for SDI allows user to create semantic model ontologies based on the extracted one or more schemas. The important functionalities achieved with APIs are: * Contextual correlation of data from different systems. * Infers & Recommends mappings between different schemas. * Import and store Semantic model. # noqa: E501

class CreateDataLakeResponse(id=None, name=None, type=None, base_path=None, created_date=None, updated_date=None)

Bases: object

Attributes:

attribute_types (dict): The key is attribute name

and the value is attribute type.

attribute_map (dict): The key is attribute name

and the value is json key in definition.

attribute_map = {'base_path': 'basePath', 'created_date': 'createdDate', 'id': 'id', 'name': 'name', 'type': 'type', 'updated_date': 'updatedDate'}

attribute_types = {'base_path': 'str', 'created_date': 'str', 'id': 'str', 'name': 'str', 'type': 'str', 'updated_date': 'str'}

base_path

Gets the base_path of this CreateDataLakeResponse.

Returns:The base_path of this CreateDataLakeResponse. Return type:str

created_date

Gets the created_date of this CreateDataLakeResponse.

Returns:The created_date of this CreateDataLakeResponse. Return type:str

id

Gets the id of this CreateDataLakeResponse.

Returns:The id of this CreateDataLakeResponse. Return type:str

name

Gets the name of this CreateDataLakeResponse.

Returns:The name of this CreateDataLakeResponse. Return type:str

to_dict()

Returns the model properties as a dict

to_str()

Returns the string representation of the model

type

Gets the type of this CreateDataLakeResponse.

Returns:The type of this CreateDataLakeResponse. Return type:str

updated_date

Gets the updated_date of this CreateDataLakeResponse.

Returns:The updated_date of this CreateDataLakeResponse. Return type:str

sdi.models.create_data_registry_request module

SDI - Semantic Data Interconnect APIs

The Semantic Data Interconnect (SDI) is a collection of APIs that allows the user to unlock the potential of disparate big data by connecting external data. The SDI can infer the schemas of data based on schema-on-read, allow creating a semantic model and perform big data semantic queries. It seamlessly connects to MindSphere’s Integrated Data Lake (IDL), but it can work independently as well. There are two mechanisms that can be used to upload files so that SDI can generate schemas and make data ready for query. The SDI operations are divided into the following groups: Data Registration for SDI This set of APIs is used to organize the incoming data. When configuring a Data Registry, you have the option to update your data based on a replace or append strategy. If you consider a use case where schema may change and incoming data files are completely different every time then replace is a good strategy. The replace strategy will replace the existing schema and data during each data ingest operation whereas the append strategy will update the existing schema and data during each data ingest operation. Custom Data Type for SDI The SDI by default identifies basic data types for each property, such as String, Integer, Float, Date, etc. The user can use this set of APIs to create their own custom data type. The SDI also provides an API under this category to suggest data type based on user-provided sample test values. Data Lake for SDI The SDI can process files uploaded provides endpoints to manage customer’s data lake registration based on tenant id, cloud provider and data lake type. The set of REST endpoint allows to create, update and retrieve base path for their data lake. The IDL customer needs to create an SDI folder that is under the root folder. Any file uploaded in this folder is automatically picked up by SDI to process via IDL notification. Data Ingest for SDI This set of APIs allows user to upload files, start an ingest job for uploaded files, find job status for ingested jobs or retrieve all job statuses. Schema Registry for SDI The SDI provides a way to find the generated schema in this category. Users can find an SDI generated schema for uploaded files based on source name, data tag or schema name. Data Query for SDI allows querying based on the extracted schemas. Important supported APIs are: * Query interface for querying semantically correlated and transformed data. * Stores and executes data queries. * Uses a semantic model to translate model-based query to physical queries. Semantic Model for SDI allows user to create semantic model ontologies based on the extracted one or more schemas. The important functionalities achieved with APIs are: * Contextual correlation of data from different systems. * Infers & Recommends mappings between different schemas. * Import and store Semantic model. # noqa: E501

class CreateDataRegistryRequest(data_tag=None, default_root_tag=None, file_pattern=None, file_upload_strategy=None, meta_data_tags=None, source_name=None, xml_process_rules=None, partition_keys=None, schema_frozen=False)

Bases: object

Attributes:

attribute_types (dict): The key is attribute name

and the value is attribute type.

attribute_map (dict): The key is attribute name

and the value is json key in definition.

attribute_map = {'data_tag': 'dataTag', 'default_root_tag': 'defaultRootTag', 'file_pattern': 'filePattern', 'file_upload_strategy': 'fileUploadStrategy', 'meta_data_tags': 'metaDataTags', 'partition_keys': 'partitionKeys', 'schema_frozen': 'schemaFrozen', 'source_name': 'sourceName', 'xml_process_rules': 'xmlProcessRules'}

attribute_types = {'data_tag': 'str', 'default_root_tag': 'str', 'file_pattern': 'str', 'file_upload_strategy': 'str', 'meta_data_tags': 'list[str]', 'partition_keys': 'list[str]', 'schema_frozen': 'bool', 'source_name': 'str', 'xml_process_rules': 'list[str]'}

data_tag

Gets the data_tag of this CreateDataRegistryRequest.

Returns:The data_tag of this CreateDataRegistryRequest. Return type:str

default_root_tag

Gets the default_root_tag of this CreateDataRegistryRequest.

Returns:The default_root_tag of this CreateDataRegistryRequest. Return type:str

file_pattern

Gets the file_pattern of this CreateDataRegistryRequest.

Returns:The file_pattern of this CreateDataRegistryRequest. Return type:str

file_upload_strategy

Gets the file_upload_strategy of this CreateDataRegistryRequest.

Returns:The file_upload_strategy of this CreateDataRegistryRequest. Return type:str

meta_data_tags

Gets the meta_data_tags of this CreateDataRegistryRequest.

Returns:The meta_data_tags of this CreateDataRegistryRequest. Return type:list[str]

partition_keys

Gets the partition_keys of this CreateDataRegistryRequest. A single paritionKey can be specified at the time of registry creation. It can be set to ‘sdi-default-partition-key’ to enable default paritioning. It can be set to a custom attribute present in the data to enable paritioning based on that attribute.

Returns:The partition_keys of this CreateDataRegistryRequest. Return type:list[str]

schema_frozen

Gets the schema_frozen of this CreateDataRegistryRequest. This property must be set to false during initial creation of a registry. It can be changed to true after the initial schema creation to reuse the existing schema for the newly ingested data

Returns:The schema_frozen of this CreateDataRegistryRequest. Return type:bool

source_name

Gets the source_name of this CreateDataRegistryRequest.

Returns:The source_name of this CreateDataRegistryRequest. Return type:str

to_dict()

Returns the model properties as a dict

to_str()

Returns the string representation of the model

xml_process_rules

Gets the xml_process_rules of this CreateDataRegistryRequest.

Returns:The xml_process_rules of this CreateDataRegistryRequest. Return type:list[str]

sdi.models.data_lake_list module

SDI - Semantic Data Interconnect APIs

The Semantic Data Interconnect (SDI) is a collection of APIs that allows the user to unlock the potential of disparate big data by connecting external data. The SDI can infer the schemas of data based on schema-on-read, allow creating a semantic model and perform big data semantic queries. It seamlessly connects to MindSphere’s Integrated Data Lake (IDL), but it can work independently as well. There are two mechanisms that can be used to upload files so that SDI can generate schemas and make data ready for query. The SDI operations are divided into the following groups: Data Registration for SDI This set of APIs is used to organize the incoming data. When configuring a Data Registry, you have the option to update your data based on a replace or append strategy. If you consider a use case where schema may change and incoming data files are completely different every time then replace is a good strategy. The replace strategy will replace the existing schema and data during each data ingest operation whereas the append strategy will update the existing schema and data during each data ingest operation. Custom Data Type for SDI The SDI by default identifies basic data types for each property, such as String, Integer, Float, Date, etc. The user can use this set of APIs to create their own custom data type. The SDI also provides an API under this category to suggest data type based on user-provided sample test values. Data Lake for SDI The SDI can process files uploaded provides endpoints to manage customer’s data lake registration based on tenant id, cloud provider and data lake type. The set of REST endpoint allows to create, update and retrieve base path for their data lake. The IDL customer needs to create an SDI folder that is under the root folder. Any file uploaded in this folder is automatically picked up by SDI to process via IDL notification. Data Ingest for SDI This set of APIs allows user to upload files, start an ingest job for uploaded files, find job status for ingested jobs or retrieve all job statuses. Schema Registry for SDI The SDI provides a way to find the generated schema in this category. Users can find an SDI generated schema for uploaded files based on source name, data tag or schema name. Data Query for SDI allows querying based on the extracted schemas. Important supported APIs are: * Query interface for querying semantically correlated and transformed data. * Stores and executes data queries. * Uses a semantic model to translate model-based query to physical queries. Semantic Model for SDI allows user to create semantic model ontologies based on the extracted one or more schemas. The important functionalities achieved with APIs are: * Contextual correlation of data from different systems. * Infers & Recommends mappings between different schemas. * Import and store Semantic model. # noqa: E501

class DataLakeList(data_lakes=None)

Bases: object

Attributes:

attribute_types (dict): The key is attribute name

and the value is attribute type.

attribute_map (dict): The key is attribute name

and the value is json key in definition.

attribute_map = {'data_lakes': 'dataLakes'}

attribute_types = {'data_lakes': 'list[DataLakeResponse]'}

data_lakes

Gets the data_lakes of this DataLakeList.

Returns:The data_lakes of this DataLakeList. Return type:list[DataLakeResponse]

to_dict()

Returns the model properties as a dict

to_str()

Returns the string representation of the model

sdi.models.data_lake_response module

SDI - Semantic Data Interconnect APIs

The Semantic Data Interconnect (SDI) is a collection of APIs that allows the user to unlock the potential of disparate big data by connecting external data. The SDI can infer the schemas of data based on schema-on-read, allow creating a semantic model and perform big data semantic queries. It seamlessly connects to MindSphere’s Integrated Data Lake (IDL), but it can work independently as well. There are two mechanisms that can be used to upload files so that SDI can generate schemas and make data ready for query. The SDI operations are divided into the following groups: Data Registration for SDI This set of APIs is used to organize the incoming data. When configuring a Data Registry, you have the option to update your data based on a replace or append strategy. If you consider a use case where schema may change and incoming data files are completely different every time then replace is a good strategy. The replace strategy will replace the existing schema and data during each data ingest operation whereas the append strategy will update the existing schema and data during each data ingest operation. Custom Data Type for SDI The SDI by default identifies basic data types for each property, such as String, Integer, Float, Date, etc. The user can use this set of APIs to create their own custom data type. The SDI also provides an API under this category to suggest data type based on user-provided sample test values. Data Lake for SDI The SDI can process files uploaded provides endpoints to manage customer’s data lake registration based on tenant id, cloud provider and data lake type. The set of REST endpoint allows to create, update and retrieve base path for their data lake. The IDL customer needs to create an SDI folder that is under the root folder. Any file uploaded in this folder is automatically picked up by SDI to process via IDL notification. Data Ingest for SDI This set of APIs allows user to upload files, start an ingest job for uploaded files, find job status for ingested jobs or retrieve all job statuses. Schema Registry for SDI The SDI provides a way to find the generated schema in this category. Users can find an SDI generated schema for uploaded files based on source name, data tag or schema name. Data Query for SDI allows querying based on the extracted schemas. Important supported APIs are: * Query interface for querying semantically correlated and transformed data. * Stores and executes data queries. * Uses a semantic model to translate model-based query to physical queries. Semantic Model for SDI allows user to create semantic model ontologies based on the extracted one or more schemas. The important functionalities achieved with APIs are: * Contextual correlation of data from different systems. * Infers & Recommends mappings between different schemas. * Import and store Semantic model. # noqa: E501

class DataLakeResponse(base_path=None, created_date=None, id=None, updated_date=None, name=None, type=None)

Bases: object

Attributes:

attribute_types (dict): The key is attribute name

and the value is attribute type.

attribute_map (dict): The key is attribute name

and the value is json key in definition.

attribute_map = {'base_path': 'basePath', 'created_date': 'createdDate', 'id': 'id', 'name': 'name', 'type': 'type', 'updated_date': 'updatedDate'}

attribute_types = {'base_path': 'str', 'created_date': 'str', 'id': 'str', 'name': 'str', 'type': 'str', 'updated_date': 'str'}

base_path

Gets the base_path of this DataLakeResponse.

Returns:The base_path of this DataLakeResponse. Return type:str

created_date

Gets the created_date of this DataLakeResponse.

Returns:The created_date of this DataLakeResponse. Return type:str

id

Gets the id of this DataLakeResponse.

Returns:The id of this DataLakeResponse. Return type:str

name

Gets the name of this DataLakeResponse.

Returns:The name of this DataLakeResponse. Return type:str

to_dict()

Returns the model properties as a dict

to_str()

Returns the string representation of the model

type

Gets the type of this DataLakeResponse.

Returns:The type of this DataLakeResponse. Return type:str

updated_date

Gets the updated_date of this DataLakeResponse.

Returns:The updated_date of this DataLakeResponse. Return type:str

sdi.models.data_query_execute_query_request module

SDI - Semantic Data Interconnect APIs

The Semantic Data Interconnect (SDI) is a collection of APIs that allows the user to unlock the potential of disparate big data by connecting external data. The SDI can infer the schemas of data based on schema-on-read, allow creating a semantic model and perform big data semantic queries. It seamlessly connects to MindSphere’s Integrated Data Lake (IDL), but it can work independently as well. There are two mechanisms that can be used to upload files so that SDI can generate schemas and make data ready for query. The SDI operations are divided into the following groups: Data Registration for SDI This set of APIs is used to organize the incoming data. When configuring a Data Registry, you have the option to update your data based on a replace or append strategy. If you consider a use case where schema may change and incoming data files are completely different every time then replace is a good strategy. The replace strategy will replace the existing schema and data during each data ingest operation whereas the append strategy will update the existing schema and data during each data ingest operation. Custom Data Type for SDI The SDI by default identifies basic data types for each property, such as String, Integer, Float, Date, etc. The user can use this set of APIs to create their own custom data type. The SDI also provides an API under this category to suggest data type based on user-provided sample test values. Data Lake for SDI The SDI can process files uploaded provides endpoints to manage customer’s data lake registration based on tenant id, cloud provider and data lake type. The set of REST endpoint allows to create, update and retrieve base path for their data lake. The IDL customer needs to create an SDI folder that is under the root folder. Any file uploaded in this folder is automatically picked up by SDI to process via IDL notification. Data Ingest for SDI This set of APIs allows user to upload files, start an ingest job for uploaded files, find job status for ingested jobs or retrieve all job statuses. Schema Registry for SDI The SDI provides a way to find the generated schema in this category. Users can find an SDI generated schema for uploaded files based on source name, data tag or schema name. Data Query for SDI allows querying based on the extracted schemas. Important supported APIs are: * Query interface for querying semantically correlated and transformed data. * Stores and executes data queries. * Uses a semantic model to translate model-based query to physical queries. Semantic Model for SDI allows user to create semantic model ontologies based on the extracted one or more schemas. The important functionalities achieved with APIs are: * Contextual correlation of data from different systems. * Infers & Recommends mappings between different schemas. * Import and store Semantic model. # noqa: E501

class DataQueryExecuteQueryRequest(description=None, parameters=None, aliases=None)

Bases: object

Attributes:

attribute_types (dict): The key is attribute name

and the value is attribute type.

attribute_map (dict): The key is attribute name

and the value is json key in definition.

aliases

Gets the aliases of this DataQueryExecuteQueryRequest.

Returns:The aliases of this DataQueryExecuteQueryRequest. Return type:list[Aliases]

attribute_map = {'aliases': 'aliases', 'description': 'description', 'parameters': 'parameters'}

attribute_types = {'aliases': 'list[Aliases]', 'description': 'str', 'parameters': 'list[Parameters]'}

description

Gets the description of this DataQueryExecuteQueryRequest.

Returns:The description of this DataQueryExecuteQueryRequest. Return type:str

parameters

Gets the parameters of this DataQueryExecuteQueryRequest.

Returns:The parameters of this DataQueryExecuteQueryRequest. Return type:list[Parameters]

to_dict()

Returns the model properties as a dict

to_str()

Returns the string representation of the model

sdi.models.data_query_execute_query_response module

SDI - Semantic Data Interconnect APIs

The Semantic Data Interconnect (SDI) is a collection of APIs that allows the user to unlock the potential of disparate big data by connecting external data. The SDI can infer the schemas of data based on schema-on-read, allow creating a semantic model and perform big data semantic queries. It seamlessly connects to MindSphere’s Integrated Data Lake (IDL), but it can work independently as well. There are two mechanisms that can be used to upload files so that SDI can generate schemas and make data ready for query. The SDI operations are divided into the following groups: Data Registration for SDI This set of APIs is used to organize the incoming data. When configuring a Data Registry, you have the option to update your data based on a replace or append strategy. If you consider a use case where schema may change and incoming data files are completely different every time then replace is a good strategy. The replace strategy will replace the existing schema and data during each data ingest operation whereas the append strategy will update the existing schema and data during each data ingest operation. Custom Data Type for SDI The SDI by default identifies basic data types for each property, such as String, Integer, Float, Date, etc. The user can use this set of APIs to create their own custom data type. The SDI also provides an API under this category to suggest data type based on user-provided sample test values. Data Lake for SDI The SDI can process files uploaded provides endpoints to manage customer’s data lake registration based on tenant id, cloud provider and data lake type. The set of REST endpoint allows to create, update and retrieve base path for their data lake. The IDL customer needs to create an SDI folder that is under the root folder. Any file uploaded in this folder is automatically picked up by SDI to process via IDL notification. Data Ingest for SDI This set of APIs allows user to upload files, start an ingest job for uploaded files, find job status for ingested jobs or retrieve all job statuses. Schema Registry for SDI The SDI provides a way to find the generated schema in this category. Users can find an SDI generated schema for uploaded files based on source name, data tag or schema name. Data Query for SDI allows querying based on the extracted schemas. Important supported APIs are: * Query interface for querying semantically correlated and transformed data. * Stores and executes data queries. * Uses a semantic model to translate model-based query to physical queries. Semantic Model for SDI allows user to create semantic model ontologies based on the extracted one or more schemas. The important functionalities achieved with APIs are: * Contextual correlation of data from different systems. * Infers & Recommends mappings between different schemas. * Import and store Semantic model. # noqa: E501

class DataQueryExecuteQueryResponse(id=None, status=None, message=None)

Bases: object

Attributes:

attribute_types (dict): The key is attribute name

and the value is attribute type.

attribute_map (dict): The key is attribute name

and the value is json key in definition.

attribute_map = {'id': 'id', 'message': 'message', 'status': 'status'}

attribute_types = {'id': 'str', 'message': 'str', 'status': 'str'}

id

Gets the id of this DataQueryExecuteQueryResponse.

Returns:The id of this DataQueryExecuteQueryResponse. Return type:str

message

Gets the message of this DataQueryExecuteQueryResponse.

Returns:The message of this DataQueryExecuteQueryResponse. Return type:str

status

Gets the status of this DataQueryExecuteQueryResponse.

Returns:The status of this DataQueryExecuteQueryResponse. Return type:str

to_dict()

Returns the model properties as a dict

to_str()

Returns the string representation of the model

sdi.models.data_query_execution_response module

SDI - Semantic Data Interconnect APIs

The Semantic Data Interconnect (SDI) is a collection of APIs that allows the user to unlock the potential of disparate big data by connecting external data. The SDI can infer the schemas of data based on schema-on-read, allow creating a semantic model and perform big data semantic queries. It seamlessly connects to MindSphere’s Integrated Data Lake (IDL), but it can work independently as well. There are two mechanisms that can be used to upload files so that SDI can generate schemas and make data ready for query. The SDI operations are divided into the following groups: Data Registration for SDI This set of APIs is used to organize the incoming data. When configuring a Data Registry, you have the option to update your data based on a replace or append strategy. If you consider a use case where schema may change and incoming data files are completely different every time then replace is a good strategy. The replace strategy will replace the existing schema and data during each data ingest operation whereas the append strategy will update the existing schema and data during each data ingest operation. Custom Data Type for SDI The SDI by default identifies basic data types for each property, such as String, Integer, Float, Date, etc. The user can use this set of APIs to create their own custom data type. The SDI also provides an API under this category to suggest data type based on user-provided sample test values. Data Lake for SDI The SDI can process files uploaded provides endpoints to manage customer’s data lake registration based on tenant id, cloud provider and data lake type. The set of REST endpoint allows to create, update and retrieve base path for their data lake. The IDL customer needs to create an SDI folder that is under the root folder. Any file uploaded in this folder is automatically picked up by SDI to process via IDL notification. Data Ingest for SDI This set of APIs allows user to upload files, start an ingest job for uploaded files, find job status for ingested jobs or retrieve all job statuses. Schema Registry for SDI The SDI provides a way to find the generated schema in this category. Users can find an SDI generated schema for uploaded files based on source name, data tag or schema name. Data Query for SDI allows querying based on the extracted schemas. Important supported APIs are: * Query interface for querying semantically correlated and transformed data. * Stores and executes data queries. * Uses a semantic model to translate model-based query to physical queries. Semantic Model for SDI allows user to create semantic model ontologies based on the extracted one or more schemas. The important functionalities achieved with APIs are: * Contextual correlation of data from different systems. * Infers & Recommends mappings between different schemas. * Import and store Semantic model. # noqa: E501

class DataQueryExecutionResponse(id=None, description=None, parameters=None, aliases=None, query_id=None, status=None, created_date=None, updated_date=None)

Bases: object

Attributes:

attribute_types (dict): The key is attribute name

and the value is attribute type.

attribute_map (dict): The key is attribute name

and the value is json key in definition.

aliases

Gets the aliases of this DataQueryExecutionResponse.

Returns:The aliases of this DataQueryExecutionResponse. Return type:list[Aliases]

attribute_map = {'aliases': 'aliases', 'created_date': 'createdDate', 'description': 'description', 'id': 'id', 'parameters': 'parameters', 'query_id': 'queryId', 'status': 'status', 'updated_date': 'updatedDate'}

attribute_types = {'aliases': 'list[Aliases]', 'created_date': 'str', 'description': 'str', 'id': 'str', 'parameters': 'list[Parameters]', 'query_id': 'str', 'status': 'str', 'updated_date': 'str'}

created_date

Gets the created_date of this DataQueryExecutionResponse.

Returns:The created_date of this DataQueryExecutionResponse. Return type:str

description

Gets the description of this DataQueryExecutionResponse.

Returns:The description of this DataQueryExecutionResponse. Return type:str

id

Gets the id of this DataQueryExecutionResponse.

Returns:The id of this DataQueryExecutionResponse. Return type:str

parameters

Gets the parameters of this DataQueryExecutionResponse.

Returns:The parameters of this DataQueryExecutionResponse. Return type:list[Parameters]

query_id

Gets the query_id of this DataQueryExecutionResponse.

Returns:The query_id of this DataQueryExecutionResponse. Return type:str

status

Gets the status of this DataQueryExecutionResponse. Status of execution job. - CURRENT: Job has executed successfully and results are current. - IN_PROGRESS: Job execution is in progress. - OUTDATED: Job execution completed but results are outdated. - FAILED: Job execution has failed. - OBSOLETE: Job execution completed but results are obsolete.

Returns:The status of this DataQueryExecutionResponse. Return type:str

to_dict()

Returns the model properties as a dict

to_str()

Returns the string representation of the model

updated_date

Gets the updated_date of this DataQueryExecutionResponse.

Returns:The updated_date of this DataQueryExecutionResponse. Return type:str

sdi.models.data_query_sql_request module

SDI - Semantic Data Interconnect APIs

The Semantic Data Interconnect (SDI) is a collection of APIs that allows the user to unlock the potential of disparate big data by connecting external data. The SDI can infer the schemas of data based on schema-on-read, allow creating a semantic model and perform big data semantic queries. It seamlessly connects to MindSphere’s Integrated Data Lake (IDL), but it can work independently as well. There are two mechanisms that can be used to upload files so that SDI can generate schemas and make data ready for query. The SDI operations are divided into the following groups: Data Registration for SDI This set of APIs is used to organize the incoming data. When configuring a Data Registry, you have the option to update your data based on a replace or append strategy. If you consider a use case where schema may change and incoming data files are completely different every time then replace is a good strategy. The replace strategy will replace the existing schema and data during each data ingest operation whereas the append strategy will update the existing schema and data during each data ingest operation. Custom Data Type for SDI The SDI by default identifies basic data types for each property, such as String, Integer, Float, Date, etc. The user can use this set of APIs to create their own custom data type. The SDI also provides an API under this category to suggest data type based on user-provided sample test values. Data Lake for SDI The SDI can process files uploaded provides endpoints to manage customer’s data lake registration based on tenant id, cloud provider and data lake type. The set of REST endpoint allows to create, update and retrieve base path for their data lake. The IDL customer needs to create an SDI folder that is under the root folder. Any file uploaded in this folder is automatically picked up by SDI to process via IDL notification. Data Ingest for SDI This set of APIs allows user to upload files, start an ingest job for uploaded files, find job status for ingested jobs or retrieve all job statuses. Schema Registry for SDI The SDI provides a way to find the generated schema in this category. Users can find an SDI generated schema for uploaded files based on source name, data tag or schema name. Data Query for SDI allows querying based on the extracted schemas. Important supported APIs are: * Query interface for querying semantically correlated and transformed data. * Stores and executes data queries. * Uses a semantic model to translate model-based query to physical queries. Semantic Model for SDI allows user to create semantic model ontologies based on the extracted one or more schemas. The important functionalities achieved with APIs are: * Contextual correlation of data from different systems. * Infers & Recommends mappings between different schemas. * Import and store Semantic model. # noqa: E501

class DataQuerySQLRequest(description=None, is_business_query=False, ontology_id=None, is_dynamic=False, name=None, sql_statement=None)

Bases: object

Attributes:

attribute_types (dict): The key is attribute name

and the value is attribute type.

attribute_map (dict): The key is attribute name

and the value is json key in definition.

attribute_map = {'description': 'description', 'is_business_query': 'isBusinessQuery', 'is_dynamic': 'isDynamic', 'name': 'name', 'ontology_id': 'ontologyId', 'sql_statement': 'sqlStatement'}

attribute_types = {'description': 'str', 'is_business_query': 'bool', 'is_dynamic': 'bool', 'name': 'str', 'ontology_id': 'str', 'sql_statement': 'str'}

description

Gets the description of this DataQuerySQLRequest.

Returns:The description of this DataQuerySQLRequest. Return type:str

is_business_query

Gets the is_business_query of this DataQuerySQLRequest.

Returns:The is_business_query of this DataQuerySQLRequest. Return type:bool

is_dynamic

Gets the is_dynamic of this DataQuerySQLRequest.

Returns:The is_dynamic of this DataQuerySQLRequest. Return type:bool

name

Gets the name of this DataQuerySQLRequest.

Returns:The name of this DataQuerySQLRequest. Return type:str

ontology_id

Gets the ontology_id of this DataQuerySQLRequest. If isBusinessQuery is true then ontologyId must be passed.

Returns:The ontology_id of this DataQuerySQLRequest. Return type:str

sql_statement

Gets the sql_statement of this DataQuerySQLRequest. Pass base64 encode value of spark sql like SELECT vehicle.vin, make.def FROM vehicle, make WHERE vehicle.make = make.id. Please refer SDI-How -to-create-query documentation for preparing sqlStatement.

Returns:The sql_statement of this DataQuerySQLRequest. Return type:str

to_dict()

Returns the model properties as a dict

to_str()

Returns the string representation of the model

sdi.models.data_query_sql_response module

SDI - Semantic Data Interconnect APIs

The Semantic Data Interconnect (SDI) is a collection of APIs that allows the user to unlock the potential of disparate big data by connecting external data. The SDI can infer the schemas of data based on schema-on-read, allow creating a semantic model and perform big data semantic queries. It seamlessly connects to MindSphere’s Integrated Data Lake (IDL), but it can work independently as well. There are two mechanisms that can be used to upload files so that SDI can generate schemas and make data ready for query. The SDI operations are divided into the following groups: Data Registration for SDI This set of APIs is used to organize the incoming data. When configuring a Data Registry, you have the option to update your data based on a replace or append strategy. If you consider a use case where schema may change and incoming data files are completely different every time then replace is a good strategy. The replace strategy will replace the existing schema and data during each data ingest operation whereas the append strategy will update the existing schema and data during each data ingest operation. Custom Data Type for SDI The SDI by default identifies basic data types for each property, such as String, Integer, Float, Date, etc. The user can use this set of APIs to create their own custom data type. The SDI also provides an API under this category to suggest data type based on user-provided sample test values. Data Lake for SDI The SDI can process files uploaded provides endpoints to manage customer’s data lake registration based on tenant id, cloud provider and data lake type. The set of REST endpoint allows to create, update and retrieve base path for their data lake. The IDL customer needs to create an SDI folder that is under the root folder. Any file uploaded in this folder is automatically picked up by SDI to process via IDL notification. Data Ingest for SDI This set of APIs allows user to upload files, start an ingest job for uploaded files, find job status for ingested jobs or retrieve all job statuses. Schema Registry for SDI The SDI provides a way to find the generated schema in this category. Users can find an SDI generated schema for uploaded files based on source name, data tag or schema name. Data Query for SDI allows querying based on the extracted schemas. Important supported APIs are: * Query interface for querying semantically correlated and transformed data. * Stores and executes data queries. * Uses a semantic model to translate model-based query to physical queries. Semantic Model for SDI allows user to create semantic model ontologies based on the extracted one or more schemas. The important functionalities achieved with APIs are: * Contextual correlation of data from different systems. * Infers & Recommends mappings between different schemas. * Import and store Semantic model. # noqa: E501

class DataQuerySQLResponse(created_date=None, description=None, executable=None, id=None, is_business_query=None, is_dynamic=None, name=None, ontology_id=None, pending_actions=None, sql_statement=None, updated_date=None)

Bases: object

Attributes:

attribute_types (dict): The key is attribute name

and the value is attribute type.

attribute_map (dict): The key is attribute name

and the value is json key in definition.

attribute_map = {'created_date': 'createdDate', 'description': 'description', 'executable': 'executable', 'id': 'id', 'is_business_query': 'isBusinessQuery', 'is_dynamic': 'isDynamic', 'name': 'name', 'ontology_id': 'ontologyId', 'pending_actions': 'pendingActions', 'sql_statement': 'sqlStatement', 'updated_date': 'updatedDate'}

attribute_types = {'created_date': 'str', 'description': 'str', 'executable': 'bool', 'id': 'str', 'is_business_query': 'bool', 'is_dynamic': 'bool', 'name': 'str', 'ontology_id': 'str', 'pending_actions': 'list[MappingErrorSQLDetails]', 'sql_statement': 'str', 'updated_date': 'str'}

created_date

Gets the created_date of this DataQuerySQLResponse.

Returns:The created_date of this DataQuerySQLResponse. Return type:str

description

Gets the description of this DataQuerySQLResponse.

Returns:The description of this DataQuerySQLResponse. Return type:str

executable

Gets the executable of this DataQuerySQLResponse.

Returns:The executable of this DataQuerySQLResponse. Return type:bool

id

Gets the id of this DataQuerySQLResponse.

Returns:The id of this DataQuerySQLResponse. Return type:str

is_business_query

Gets the is_business_query of this DataQuerySQLResponse.

Returns:The is_business_query of this DataQuerySQLResponse. Return type:bool

is_dynamic

Gets the is_dynamic of this DataQuerySQLResponse.

Returns:The is_dynamic of this DataQuerySQLResponse. Return type:bool

name

Gets the name of this DataQuerySQLResponse.

Returns:The name of this DataQuerySQLResponse. Return type:str

ontology_id

Gets the ontology_id of this DataQuerySQLResponse.

Returns:The ontology_id of this DataQuerySQLResponse. Return type:str

pending_actions

Gets the pending_actions of this DataQuerySQLResponse.

Returns:The pending_actions of this DataQuerySQLResponse. Return type:list[MappingErrorSQLDetails]

sql_statement

Gets the sql_statement of this DataQuerySQLResponse.

Returns:The sql_statement of this DataQuerySQLResponse. Return type:str

to_dict()

Returns the model properties as a dict

to_str()

Returns the string representation of the model

updated_date

Gets the updated_date of this DataQuerySQLResponse.

Returns:The updated_date of this DataQuerySQLResponse. Return type:str

sdi.models.data_query_sql_update_request module

SDI - Semantic Data Interconnect APIs

The Semantic Data Interconnect (SDI) is a collection of APIs that allows the user to unlock the potential of disparate big data by connecting external data. The SDI can infer the schemas of data based on schema-on-read, allow creating a semantic model and perform big data semantic queries. It seamlessly connects to MindSphere’s Integrated Data Lake (IDL), but it can work independently as well. There are two mechanisms that can be used to upload files so that SDI can generate schemas and make data ready for query. The SDI operations are divided into the following groups: Data Registration for SDI This set of APIs is used to organize the incoming data. When configuring a Data Registry, you have the option to update your data based on a replace or append strategy. If you consider a use case where schema may change and incoming data files are completely different every time then replace is a good strategy. The replace strategy will replace the existing schema and data during each data ingest operation whereas the append strategy will update the existing schema and data during each data ingest operation. Custom Data Type for SDI The SDI by default identifies basic data types for each property, such as String, Integer, Float, Date, etc. The user can use this set of APIs to create their own custom data type. The SDI also provides an API under this category to suggest data type based on user-provided sample test values. Data Lake for SDI The SDI can process files uploaded provides endpoints to manage customer’s data lake registration based on tenant id, cloud provider and data lake type. The set of REST endpoint allows to create, update and retrieve base path for their data lake. The IDL customer needs to create an SDI folder that is under the root folder. Any file uploaded in this folder is automatically picked up by SDI to process via IDL notification. Data Ingest for SDI This set of APIs allows user to upload files, start an ingest job for uploaded files, find job status for ingested jobs or retrieve all job statuses. Schema Registry for SDI The SDI provides a way to find the generated schema in this category. Users can find an SDI generated schema for uploaded files based on source name, data tag or schema name. Data Query for SDI allows querying based on the extracted schemas. Important supported APIs are: * Query interface for querying semantically correlated and transformed data. * Stores and executes data queries. * Uses a semantic model to translate model-based query to physical queries. Semantic Model for SDI allows user to create semantic model ontologies based on the extracted one or more schemas. The important functionalities achieved with APIs are: * Contextual correlation of data from different systems. * Infers & Recommends mappings between different schemas. * Import and store Semantic model. # noqa: E501

class DataQuerySQLUpdateRequest(description=None, is_business_query=False, ontology_id=None, is_dynamic=None, name=None, sql_statement=None)

Bases: object

Attributes:

attribute_types (dict): The key is attribute name

and the value is attribute type.

attribute_map (dict): The key is attribute name

and the value is json key in definition.

attribute_map = {'description': 'description', 'is_business_query': 'isBusinessQuery', 'is_dynamic': 'isDynamic', 'name': 'name', 'ontology_id': 'ontologyId', 'sql_statement': 'sqlStatement'}

attribute_types = {'description': 'str', 'is_business_query': 'bool', 'is_dynamic': 'bool', 'name': 'str', 'ontology_id': 'str', 'sql_statement': 'str'}

description

Gets the description of this DataQuerySQLUpdateRequest.

Returns:The description of this DataQuerySQLUpdateRequest. Return type:str

is_business_query

Gets the is_business_query of this DataQuerySQLUpdateRequest.

Returns:The is_business_query of this DataQuerySQLUpdateRequest. Return type:bool

is_dynamic

Gets the is_dynamic of this DataQuerySQLUpdateRequest.

Returns:The is_dynamic of this DataQuerySQLUpdateRequest. Return type:bool

name

Gets the name of this DataQuerySQLUpdateRequest.

Returns:The name of this DataQuerySQLUpdateRequest. Return type:str

ontology_id

Gets the ontology_id of this DataQuerySQLUpdateRequest. If isBusinessQuery is true then ontologyId must be passed.

Returns:The ontology_id of this DataQuerySQLUpdateRequest. Return type:str

sql_statement

Gets the sql_statement of this DataQuerySQLUpdateRequest. Pass base64 encode value of spark sql like SELECT vehicle.vin, make.def FROM vehicle, make WHERE vehicle.make = make.id. Please refer SDI-How -to-create-query documentation for preparing sqlStatement.

Returns:The sql_statement of this DataQuerySQLUpdateRequest. Return type:str

to_dict()

Returns the model properties as a dict

to_str()

Returns the string representation of the model

sdi.models.data_registry module

SDI - Semantic Data Interconnect APIs

The Semantic Data Interconnect (SDI) is a collection of APIs that allows the user to unlock the potential of disparate big data by connecting external data. The SDI can infer the schemas of data based on schema-on-read, allow creating a semantic model and perform big data semantic queries. It seamlessly connects to MindSphere’s Integrated Data Lake (IDL), but it can work independently as well. There are two mechanisms that can be used to upload files so that SDI can generate schemas and make data ready for query. The SDI operations are divided into the following groups: Data Registration for SDI This set of APIs is used to organize the incoming data. When configuring a Data Registry, you have the option to update your data based on a replace or append strategy. If you consider a use case where schema may change and incoming data files are completely different every time then replace is a good strategy. The replace strategy will replace the existing schema and data during each data ingest operation whereas the append strategy will update the existing schema and data during each data ingest operation. Custom Data Type for SDI The SDI by default identifies basic data types for each property, such as String, Integer, Float, Date, etc. The user can use this set of APIs to create their own custom data type. The SDI also provides an API under this category to suggest data type based on user-provided sample test values. Data Lake for SDI The SDI can process files uploaded provides endpoints to manage customer’s data lake registration based on tenant id, cloud provider and data lake type. The set of REST endpoint allows to create, update and retrieve base path for their data lake. The IDL customer needs to create an SDI folder that is under the root folder. Any file uploaded in this folder is automatically picked up by SDI to process via IDL notification. Data Ingest for SDI This set of APIs allows user to upload files, start an ingest job for uploaded files, find job status for ingested jobs or retrieve all job statuses. Schema Registry for SDI The SDI provides a way to find the generated schema in this category. Users can find an SDI generated schema for uploaded files based on source name, data tag or schema name. Data Query for SDI allows querying based on the extracted schemas. Important supported APIs are: * Query interface for querying semantically correlated and transformed data. * Stores and executes data queries. * Uses a semantic model to translate model-based query to physical queries. Semantic Model for SDI allows user to create semantic model ontologies based on the extracted one or more schemas. The important functionalities achieved with APIs are: * Contextual correlation of data from different systems. * Infers & Recommends mappings between different schemas. * Import and store Semantic model. # noqa: E501

class DataRegistry(created_date=None, data_tag=None, default_root_tag=None, file_pattern=None, file_upload_strategy=None, updated_date=None, meta_data_tags=None, xml_process_rules=None, partition_keys=None, mutable=None, registry_id=None, source_id=None, source_name=None, schema_frozen=None)

Bases: object

Attributes:

attribute_types (dict): The key is attribute name

and the value is attribute type.

attribute_map (dict): The key is attribute name

and the value is json key in definition.

attribute_map = {'created_date': 'createdDate', 'data_tag': 'dataTag', 'default_root_tag': 'defaultRootTag', 'file_pattern': 'filePattern', 'file_upload_strategy': 'fileUploadStrategy', 'meta_data_tags': 'metaDataTags', 'mutable': 'mutable', 'partition_keys': 'partitionKeys', 'registry_id': 'registryId', 'schema_frozen': 'schemaFrozen', 'source_id': 'sourceId', 'source_name': 'sourceName', 'updated_date': 'updatedDate', 'xml_process_rules': 'xmlProcessRules'}

attribute_types = {'created_date': 'str', 'data_tag': 'str', 'default_root_tag': 'str', 'file_pattern': 'str', 'file_upload_strategy': 'str', 'meta_data_tags': 'list[str]', 'mutable': 'bool', 'partition_keys': 'list[str]', 'registry_id': 'str', 'schema_frozen': 'bool', 'source_id': 'str', 'source_name': 'str', 'updated_date': 'str', 'xml_process_rules': 'list[str]'}

created_date

Gets the created_date of this DataRegistry.

Returns:The created_date of this DataRegistry. Return type:str

data_tag

Gets the data_tag of this DataRegistry.

Returns:The data_tag of this DataRegistry. Return type:str

default_root_tag

Gets the default_root_tag of this DataRegistry.

Returns:The default_root_tag of this DataRegistry. Return type:str

file_pattern

Gets the file_pattern of this DataRegistry.

Returns:The file_pattern of this DataRegistry. Return type:str

file_upload_strategy

Gets the file_upload_strategy of this DataRegistry.

Returns:The file_upload_strategy of this DataRegistry. Return type:str

meta_data_tags

Gets the meta_data_tags of this DataRegistry.

Returns:The meta_data_tags of this DataRegistry. Return type:list[str]

mutable

Gets the mutable of this DataRegistry.

Returns:The mutable of this DataRegistry. Return type:bool

partition_keys

Gets the partition_keys of this DataRegistry.

Returns:The partition_keys of this DataRegistry. Return type:list[str]

registry_id

Gets the registry_id of this DataRegistry.

Returns:The registry_id of this DataRegistry. Return type:str

schema_frozen

Gets the schema_frozen of this DataRegistry.

Returns:The schema_frozen of this DataRegistry. Return type:bool

source_id

Gets the source_id of this DataRegistry.

Returns:The source_id of this DataRegistry. Return type:str

source_name

Gets the source_name of this DataRegistry.

Returns:The source_name of this DataRegistry. Return type:str

to_dict()

Returns the model properties as a dict

to_str()

Returns the string representation of the model

updated_date

Gets the updated_date of this DataRegistry.

Returns:The updated_date of this DataRegistry. Return type:str

xml_process_rules

Gets the xml_process_rules of this DataRegistry.

Returns:The xml_process_rules of this DataRegistry. Return type:list[str]

sdi.models.data_type_definition module

SDI - Semantic Data Interconnect APIs

The Semantic Data Interconnect (SDI) is a collection of APIs that allows the user to unlock the potential of disparate big data by connecting external data. The SDI can infer the schemas of data based on schema-on-read, allow creating a semantic model and perform big data semantic queries. It seamlessly connects to MindSphere’s Integrated Data Lake (IDL), but it can work independently as well. There are two mechanisms that can be used to upload files so that SDI can generate schemas and make data ready for query. The SDI operations are divided into the following groups: Data Registration for SDI This set of APIs is used to organize the incoming data. When configuring a Data Registry, you have the option to update your data based on a replace or append strategy. If you consider a use case where schema may change and incoming data files are completely different every time then replace is a good strategy. The replace strategy will replace the existing schema and data during each data ingest operation whereas the append strategy will update the existing schema and data during each data ingest operation. Custom Data Type for SDI The SDI by default identifies basic data types for each property, such as String, Integer, Float, Date, etc. The user can use this set of APIs to create their own custom data type. The SDI also provides an API under this category to suggest data type based on user-provided sample test values. Data Lake for SDI The SDI can process files uploaded provides endpoints to manage customer’s data lake registration based on tenant id, cloud provider and data lake type. The set of REST endpoint allows to create, update and retrieve base path for their data lake. The IDL customer needs to create an SDI folder that is under the root folder. Any file uploaded in this folder is automatically picked up by SDI to process via IDL notification. Data Ingest for SDI This set of APIs allows user to upload files, start an ingest job for uploaded files, find job status for ingested jobs or retrieve all job statuses. Schema Registry for SDI The SDI provides a way to find the generated schema in this category. Users can find an SDI generated schema for uploaded files based on source name, data tag or schema name. Data Query for SDI allows querying based on the extracted schemas. Important supported APIs are: * Query interface for querying semantically correlated and transformed data. * Stores and executes data queries. * Uses a semantic model to translate model-based query to physical queries. Semantic Model for SDI allows user to create semantic model ontologies based on the extracted one or more schemas. The important functionalities achieved with APIs are: * Contextual correlation of data from different systems. * Infers & Recommends mappings between different schemas. * Import and store Semantic model. # noqa: E501

class DataTypeDefinition(name=None, patterns=None)

Bases: object

Attributes:

attribute_types (dict): The key is attribute name

and the value is attribute type.

attribute_map (dict): The key is attribute name

and the value is json key in definition.

attribute_map = {'name': 'name', 'patterns': 'patterns'}

attribute_types = {'name': 'str', 'patterns': 'list[str]'}

name

Gets the name of this DataTypeDefinition.

Returns:The name of this DataTypeDefinition. Return type:str

patterns

Gets the patterns of this DataTypeDefinition.

Returns:The patterns of this DataTypeDefinition. Return type:list[str]

to_dict()

Returns the model properties as a dict

to_str()

Returns the string representation of the model

sdi.models.data_type_pattern module

SDI - Semantic Data Interconnect APIs

The Semantic Data Interconnect (SDI) is a collection of APIs that allows the user to unlock the potential of disparate big data by connecting external data. The SDI can infer the schemas of data based on schema-on-read, allow creating a semantic model and perform big data semantic queries. It seamlessly connects to MindSphere’s Integrated Data Lake (IDL), but it can work independently as well. There are two mechanisms that can be used to upload files so that SDI can generate schemas and make data ready for query. The SDI operations are divided into the following groups: Data Registration for SDI This set of APIs is used to organize the incoming data. When configuring a Data Registry, you have the option to update your data based on a replace or append strategy. If you consider a use case where schema may change and incoming data files are completely different every time then replace is a good strategy. The replace strategy will replace the existing schema and data during each data ingest operation whereas the append strategy will update the existing schema and data during each data ingest operation. Custom Data Type for SDI The SDI by default identifies basic data types for each property, such as String, Integer, Float, Date, etc. The user can use this set of APIs to create their own custom data type. The SDI also provides an API under this category to suggest data type based on user-provided sample test values. Data Lake for SDI The SDI can process files uploaded provides endpoints to manage customer’s data lake registration based on tenant id, cloud provider and data lake type. The set of REST endpoint allows to create, update and retrieve base path for their data lake. The IDL customer needs to create an SDI folder that is under the root folder. Any file uploaded in this folder is automatically picked up by SDI to process via IDL notification. Data Ingest for SDI This set of APIs allows user to upload files, start an ingest job for uploaded files, find job status for ingested jobs or retrieve all job statuses. Schema Registry for SDI The SDI provides a way to find the generated schema in this category. Users can find an SDI generated schema for uploaded files based on source name, data tag or schema name. Data Query for SDI allows querying based on the extracted schemas. Important supported APIs are: * Query interface for querying semantically correlated and transformed data. * Stores and executes data queries. * Uses a semantic model to translate model-based query to physical queries. Semantic Model for SDI allows user to create semantic model ontologies based on the extracted one or more schemas. The important functionalities achieved with APIs are: * Contextual correlation of data from different systems. * Infers & Recommends mappings between different schemas. * Import and store Semantic model. # noqa: E501

class DataTypePattern(patterns=None)

Bases: object

Attributes:

attribute_types (dict): The key is attribute name

and the value is attribute type.

attribute_map (dict): The key is attribute name

and the value is json key in definition.

attribute_map = {'patterns': 'patterns'}

attribute_types = {'patterns': 'list[str]'}

patterns

Gets the patterns of this DataTypePattern.

Returns:The patterns of this DataTypePattern. Return type:list[str]

to_dict()

Returns the model properties as a dict

to_str()

Returns the string representation of the model

sdi.models.error_message module

SDI - Semantic Data Interconnect APIs

The Semantic Data Interconnect (SDI) is a collection of APIs that allows the user to unlock the potential of disparate big data by connecting external data. The SDI can infer the schemas of data based on schema-on-read, allow creating a semantic model and perform big data semantic queries. It seamlessly connects to MindSphere’s Integrated Data Lake (IDL), but it can work independently as well. There are two mechanisms that can be used to upload files so that SDI can generate schemas and make data ready for query. The SDI operations are divided into the following groups: Data Registration for SDI This set of APIs is used to organize the incoming data. When configuring a Data Registry, you have the option to update your data based on a replace or append strategy. If you consider a use case where schema may change and incoming data files are completely different every time then replace is a good strategy. The replace strategy will replace the existing schema and data during each data ingest operation whereas the append strategy will update the existing schema and data during each data ingest operation. Custom Data Type for SDI The SDI by default identifies basic data types for each property, such as String, Integer, Float, Date, etc. The user can use this set of APIs to create their own custom data type. The SDI also provides an API under this category to suggest data type based on user-provided sample test values. Data Lake for SDI The SDI can process files uploaded provides endpoints to manage customer’s data lake registration based on tenant id, cloud provider and data lake type. The set of REST endpoint allows to create, update and retrieve base path for their data lake. The IDL customer needs to create an SDI folder that is under the root folder. Any file uploaded in this folder is automatically picked up by SDI to process via IDL notification. Data Ingest for SDI This set of APIs allows user to upload files, start an ingest job for uploaded files, find job status for ingested jobs or retrieve all job statuses. Schema Registry for SDI The SDI provides a way to find the generated schema in this category. Users can find an SDI generated schema for uploaded files based on source name, data tag or schema name. Data Query for SDI allows querying based on the extracted schemas. Important supported APIs are: * Query interface for querying semantically correlated and transformed data. * Stores and executes data queries. * Uses a semantic model to translate model-based query to physical queries. Semantic Model for SDI allows user to create semantic model ontologies based on the extracted one or more schemas. The important functionalities achieved with APIs are: * Contextual correlation of data from different systems. * Infers & Recommends mappings between different schemas. * Import and store Semantic model. # noqa: E501

class ErrorMessage(errors=None)

Bases: object

Attributes:

attribute_types (dict): The key is attribute name

and the value is attribute type.

attribute_map (dict): The key is attribute name

and the value is json key in definition.

attribute_map = {'errors': 'errors'}

attribute_types = {'errors': 'list[ApiFieldError]'}

errors

Gets the errors of this ErrorMessage.

Returns:The errors of this ErrorMessage. Return type:list[ApiFieldError]

to_dict()

Returns the model properties as a dict

to_str()

Returns the string representation of the model

sdi.models.file module

SDI - Semantic Data Interconnect APIs

The Semantic Data Interconnect (SDI) is a collection of APIs that allows the user to unlock the potential of disparate big data by connecting external data. The SDI can infer the schemas of data based on schema-on-read, allow creating a semantic model and perform big data semantic queries. It seamlessly connects to MindSphere’s Integrated Data Lake (IDL), but it can work independently as well. There are two mechanisms that can be used to upload files so that SDI can generate schemas and make data ready for query. The SDI operations are divided into the following groups: Data Registration for SDI This set of APIs is used to organize the incoming data. When configuring a Data Registry, you have the option to update your data based on a replace or append strategy. If you consider a use case where schema may change and incoming data files are completely different every time then replace is a good strategy. The replace strategy will replace the existing schema and data during each data ingest operation whereas the append strategy will update the existing schema and data during each data ingest operation. Custom Data Type for SDI The SDI by default identifies basic data types for each property, such as String, Integer, Float, Date, etc. The user can use this set of APIs to create their own custom data type. The SDI also provides an API under this category to suggest data type based on user-provided sample test values. Data Lake for SDI The SDI can process files uploaded provides endpoints to manage customer’s data lake registration based on tenant id, cloud provider and data lake type. The set of REST endpoint allows to create, update and retrieve base path for their data lake. The IDL customer needs to create an SDI folder that is under the root folder. Any file uploaded in this folder is automatically picked up by SDI to process via IDL notification. Data Ingest for SDI This set of APIs allows user to upload files, start an ingest job for uploaded files, find job status for ingested jobs or retrieve all job statuses. Schema Registry for SDI The SDI provides a way to find the generated schema in this category. Users can find an SDI generated schema for uploaded files based on source name, data tag or schema name. Data Query for SDI allows querying based on the extracted schemas. Important supported APIs are: * Query interface for querying semantically correlated and transformed data. * Stores and executes data queries. * Uses a semantic model to translate model-based query to physical queries. Semantic Model for SDI allows user to create semantic model ontologies based on the extracted one or more schemas. The important functionalities achieved with APIs are: * Contextual correlation of data from different systems. * Infers & Recommends mappings between different schemas. * Import and store Semantic model. # noqa: E501

class FileInput(file=None)

Bases: object

Attributes:

attribute_types (dict): The key is attribute name

and the value is attribute type.

attribute_map (dict): The key is attribute name

and the value is json key in definition.

attribute_map = {'file': 'file'}

attribute_types = {'file': 'file'}

file

Gets the file of this FileInput.

Returns:The file of this FileInput. Return type:file

to_dict()

Returns the model properties as a dict

to_str()

Returns the string representation of the model

sdi.models.get_all_sql_queries_data module

SDI - Semantic Data Interconnect APIs

The Semantic Data Interconnect (SDI) is a collection of APIs that allows the user to unlock the potential of disparate big data by connecting external data. The SDI can infer the schemas of data based on schema-on-read, allow creating a semantic model and perform big data semantic queries. It seamlessly connects to MindSphere’s Integrated Data Lake (IDL), but it can work independently as well. There are two mechanisms that can be used to upload files so that SDI can generate schemas and make data ready for query. The SDI operations are divided into the following groups: Data Registration for SDI This set of APIs is used to organize the incoming data. When configuring a Data Registry, you have the option to update your data based on a replace or append strategy. If you consider a use case where schema may change and incoming data files are completely different every time then replace is a good strategy. The replace strategy will replace the existing schema and data during each data ingest operation whereas the append strategy will update the existing schema and data during each data ingest operation. Custom Data Type for SDI The SDI by default identifies basic data types for each property, such as String, Integer, Float, Date, etc. The user can use this set of APIs to create their own custom data type. The SDI also provides an API under this category to suggest data type based on user-provided sample test values. Data Lake for SDI The SDI can process files uploaded provides endpoints to manage customer’s data lake registration based on tenant id, cloud provider and data lake type. The set of REST endpoint allows to create, update and retrieve base path for their data lake. The IDL customer needs to create an SDI folder that is under the root folder. Any file uploaded in this folder is automatically picked up by SDI to process via IDL notification. Data Ingest for SDI This set of APIs allows user to upload files, start an ingest job for uploaded files, find job status for ingested jobs or retrieve all job statuses. Schema Registry for SDI The SDI provides a way to find the generated schema in this category. Users can find an SDI generated schema for uploaded files based on source name, data tag or schema name. Data Query for SDI allows querying based on the extracted schemas. Important supported APIs are: * Query interface for querying semantically correlated and transformed data. * Stores and executes data queries. * Uses a semantic model to translate model-based query to physical queries. Semantic Model for SDI allows user to create semantic model ontologies based on the extracted one or more schemas. The important functionalities achieved with APIs are: * Contextual correlation of data from different systems. * Infers & Recommends mappings between different schemas. * Import and store Semantic model. # noqa: E501

class GetAllSQLQueriesData(created_by=None, created_date=None, description=None, executable=None, id=None, is_business_query=None, is_dynamic=None, name=None, ontology_id=None, pending_actions=None, updated_by=None, updated_date=None)

Bases: object

Attributes:

attribute_types (dict): The key is attribute name

and the value is attribute type.

attribute_map (dict): The key is attribute name

and the value is json key in definition.

attribute_map = {'created_by': 'createdBy', 'created_date': 'createdDate', 'description': 'description', 'executable': 'executable', 'id': 'id', 'is_business_query': 'isBusinessQuery', 'is_dynamic': 'isDynamic', 'name': 'name', 'ontology_id': 'ontologyId', 'pending_actions': 'pendingActions', 'updated_by': 'updatedBy', 'updated_date': 'updatedDate'}

attribute_types = {'created_by': 'str', 'created_date': 'str', 'description': 'str', 'executable': 'bool', 'id': 'str', 'is_business_query': 'bool', 'is_dynamic': 'bool', 'name': 'str', 'ontology_id': 'str', 'pending_actions': 'list[MappingErrorSQLDetails]', 'updated_by': 'str', 'updated_date': 'str'}

created_by

Gets the created_by of this GetAllSQLQueriesData.

Returns:The created_by of this GetAllSQLQueriesData. Return type:str

created_date

Gets the created_date of this GetAllSQLQueriesData.

Returns:The created_date of this GetAllSQLQueriesData. Return type:str

description

Gets the description of this GetAllSQLQueriesData.

Returns:The description of this GetAllSQLQueriesData. Return type:str

executable

Gets the executable of this GetAllSQLQueriesData.

Returns:The executable of this GetAllSQLQueriesData. Return type:bool

id

Gets the id of this GetAllSQLQueriesData.

Returns:The id of this GetAllSQLQueriesData. Return type:str

is_business_query

Gets the is_business_query of this GetAllSQLQueriesData.

Returns:The is_business_query of this GetAllSQLQueriesData. Return type:bool

is_dynamic

Gets the is_dynamic of this GetAllSQLQueriesData.

Returns:The is_dynamic of this GetAllSQLQueriesData. Return type:bool

name

Gets the name of this GetAllSQLQueriesData.

Returns:The name of this GetAllSQLQueriesData. Return type:str

ontology_id

Gets the ontology_id of this GetAllSQLQueriesData.

Returns:The ontology_id of this GetAllSQLQueriesData. Return type:str

pending_actions

Gets the pending_actions of this GetAllSQLQueriesData.

Returns:The pending_actions of this GetAllSQLQueriesData. Return type:list[MappingErrorSQLDetails]

to_dict()

Returns the model properties as a dict

to_str()

Returns the string representation of the model

updated_by

Gets the updated_by of this GetAllSQLQueriesData.

Returns:The updated_by of this GetAllSQLQueriesData. Return type:str

updated_date

Gets the updated_date of this GetAllSQLQueriesData.

Returns:The updated_date of this GetAllSQLQueriesData. Return type:str

sdi.models.infer_schema_search_request module

SDI - Semantic Data Interconnect APIs

The Semantic Data Interconnect (SDI) is a collection of APIs that allows the user to unlock the potential of disparate big data by connecting external data. The SDI can infer the schemas of data based on schema-on-read, allow creating a semantic model and perform big data semantic queries. It seamlessly connects to MindSphere’s Integrated Data Lake (IDL), but it can work independently as well. There are two mechanisms that can be used to upload files so that SDI can generate schemas and make data ready for query. The SDI operations are divided into the following groups: Data Registration for SDI This set of APIs is used to organize the incoming data. When configuring a Data Registry, you have the option to update your data based on a replace or append strategy. If you consider a use case where schema may change and incoming data files are completely different every time then replace is a good strategy. The replace strategy will replace the existing schema and data during each data ingest operation whereas the append strategy will update the existing schema and data during each data ingest operation. Custom Data Type for SDI The SDI by default identifies basic data types for each property, such as String, Integer, Float, Date, etc. The user can use this set of APIs to create their own custom data type. The SDI also provides an API under this category to suggest data type based on user-provided sample test values. Data Lake for SDI The SDI can process files uploaded provides endpoints to manage customer’s data lake registration based on tenant id, cloud provider and data lake type. The set of REST endpoint allows to create, update and retrieve base path for their data lake. The IDL customer needs to create an SDI folder that is under the root folder. Any file uploaded in this folder is automatically picked up by SDI to process via IDL notification. Data Ingest for SDI This set of APIs allows user to upload files, start an ingest job for uploaded files, find job status for ingested jobs or retrieve all job statuses. Schema Registry for SDI The SDI provides a way to find the generated schema in this category. Users can find an SDI generated schema for uploaded files based on source name, data tag or schema name. Data Query for SDI allows querying based on the extracted schemas. Important supported APIs are: * Query interface for querying semantically correlated and transformed data. * Stores and executes data queries. * Uses a semantic model to translate model-based query to physical queries. Semantic Model for SDI allows user to create semantic model ontologies based on the extracted one or more schemas. The important functionalities achieved with APIs are: * Contextual correlation of data from different systems. * Infers & Recommends mappings between different schemas. * Import and store Semantic model. # noqa: E501

class InferSchemaSearchRequest(schemas=None, exclude_properties=None)

Bases: object

Attributes:

attribute_types (dict): The key is attribute name

and the value is attribute type.

attribute_map (dict): The key is attribute name

and the value is json key in definition.

attribute_map = {'exclude_properties': 'excludeProperties', 'schemas': 'schemas'}

attribute_types = {'exclude_properties': 'list[str]', 'schemas': 'list[InferSearchObject]'}

exclude_properties

Gets the exclude_properties of this InferSchemaSearchRequest.

Returns:The exclude_properties of this InferSchemaSearchRequest. Return type:list[str]

schemas

Gets the schemas of this InferSchemaSearchRequest.

Returns:The schemas of this InferSchemaSearchRequest. Return type:list[InferSearchObject]

to_dict()

Returns the model properties as a dict

to_str()

Returns the string representation of the model

sdi.models.infer_search_object module

SDI - Semantic Data Interconnect APIs

The Semantic Data Interconnect (SDI) is a collection of APIs that allows the user to unlock the potential of disparate big data by connecting external data. The SDI can infer the schemas of data based on schema-on-read, allow creating a semantic model and perform big data semantic queries. It seamlessly connects to MindSphere’s Integrated Data Lake (IDL), but it can work independently as well. There are two mechanisms that can be used to upload files so that SDI can generate schemas and make data ready for query. The SDI operations are divided into the following groups: Data Registration for SDI This set of APIs is used to organize the incoming data. When configuring a Data Registry, you have the option to update your data based on a replace or append strategy. If you consider a use case where schema may change and incoming data files are completely different every time then replace is a good strategy. The replace strategy will replace the existing schema and data during each data ingest operation whereas the append strategy will update the existing schema and data during each data ingest operation. Custom Data Type for SDI The SDI by default identifies basic data types for each property, such as String, Integer, Float, Date, etc. The user can use this set of APIs to create their own custom data type. The SDI also provides an API under this category to suggest data type based on user-provided sample test values. Data Lake for SDI The SDI can process files uploaded provides endpoints to manage customer’s data lake registration based on tenant id, cloud provider and data lake type. The set of REST endpoint allows to create, update and retrieve base path for their data lake. The IDL customer needs to create an SDI folder that is under the root folder. Any file uploaded in this folder is automatically picked up by SDI to process via IDL notification. Data Ingest for SDI This set of APIs allows user to upload files, start an ingest job for uploaded files, find job status for ingested jobs or retrieve all job statuses. Schema Registry for SDI The SDI provides a way to find the generated schema in this category. Users can find an SDI generated schema for uploaded files based on source name, data tag or schema name. Data Query for SDI allows querying based on the extracted schemas. Important supported APIs are: * Query interface for querying semantically correlated and transformed data. * Stores and executes data queries. * Uses a semantic model to translate model-based query to physical queries. Semantic Model for SDI allows user to create semantic model ontologies based on the extracted one or more schemas. The important functionalities achieved with APIs are: * Contextual correlation of data from different systems. * Infers & Recommends mappings between different schemas. * Import and store Semantic model. # noqa: E501

class InferSearchObject(data_tag=None, schema_name=None, source_name=None, asset_id=None, aspect_name=None)

Bases: object

Attributes:

attribute_types (dict): The key is attribute name

and the value is attribute type.

attribute_map (dict): The key is attribute name

and the value is json key in definition.

aspect_name

Gets the aspect_name of this InferSearchObject.

Returns:The aspect_name of this InferSearchObject. Return type:str

asset_id

Gets the asset_id of this InferSearchObject.

Returns:The asset_id of this InferSearchObject. Return type:str

attribute_map = {'aspect_name': 'aspectName', 'asset_id': 'assetId', 'data_tag': 'dataTag', 'schema_name': 'schemaName', 'source_name': 'sourceName'}

attribute_types = {'aspect_name': 'str', 'asset_id': 'str', 'data_tag': 'str', 'schema_name': 'str', 'source_name': 'str'}

data_tag

Gets the data_tag of this InferSearchObject.

Returns:The data_tag of this InferSearchObject. Return type:str

schema_name

Gets the schema_name of this InferSearchObject.

Returns:The schema_name of this InferSearchObject. Return type:str

source_name

Gets the source_name of this InferSearchObject.

Returns:The source_name of this InferSearchObject. Return type:str

to_dict()

Returns the model properties as a dict

to_str()

Returns the string representation of the model

sdi.models.input_class module

SDI - Semantic Data Interconnect APIs

The Semantic Data Interconnect (SDI) is a collection of APIs that allows the user to unlock the potential of disparate big data by connecting external data. The SDI can infer the schemas of data based on schema-on-read, allow creating a semantic model and perform big data semantic queries. It seamlessly connects to MindSphere’s Integrated Data Lake (IDL), but it can work independently as well. There are two mechanisms that can be used to upload files so that SDI can generate schemas and make data ready for query. The SDI operations are divided into the following groups: Data Registration for SDI This set of APIs is used to organize the incoming data. When configuring a Data Registry, you have the option to update your data based on a replace or append strategy. If you consider a use case where schema may change and incoming data files are completely different every time then replace is a good strategy. The replace strategy will replace the existing schema and data during each data ingest operation whereas the append strategy will update the existing schema and data during each data ingest operation. Custom Data Type for SDI The SDI by default identifies basic data types for each property, such as String, Integer, Float, Date, etc. The user can use this set of APIs to create their own custom data type. The SDI also provides an API under this category to suggest data type based on user-provided sample test values. Data Lake for SDI The SDI can process files uploaded provides endpoints to manage customer’s data lake registration based on tenant id, cloud provider and data lake type. The set of REST endpoint allows to create, update and retrieve base path for their data lake. The IDL customer needs to create an SDI folder that is under the root folder. Any file uploaded in this folder is automatically picked up by SDI to process via IDL notification. Data Ingest for SDI This set of APIs allows user to upload files, start an ingest job for uploaded files, find job status for ingested jobs or retrieve all job statuses. Schema Registry for SDI The SDI provides a way to find the generated schema in this category. Users can find an SDI generated schema for uploaded files based on source name, data tag or schema name. Data Query for SDI allows querying based on the extracted schemas. Important supported APIs are: * Query interface for querying semantically correlated and transformed data. * Stores and executes data queries. * Uses a semantic model to translate model-based query to physical queries. Semantic Model for SDI allows user to create semantic model ontologies based on the extracted one or more schemas. The important functionalities achieved with APIs are: * Contextual correlation of data from different systems. * Infers & Recommends mappings between different schemas. * Import and store Semantic model. # noqa: E501

class InputClass(description=None, name=None, primary_schema=None, key_mapping_type=None)

Bases: object

Attributes:

attribute_types (dict): The key is attribute name

and the value is attribute type.

attribute_map (dict): The key is attribute name

and the value is json key in definition.

attribute_map = {'description': 'description', 'key_mapping_type': 'keyMappingType', 'name': 'name', 'primary_schema': 'primarySchema'}

attribute_types = {'description': 'str', 'key_mapping_type': 'str', 'name': 'str', 'primary_schema': 'str'}

description

Gets the description of this InputClass.

Returns:The description of this InputClass. Return type:str

key_mapping_type

Gets the key_mapping_type of this InputClass. Class keyMappingType. If Parent level ‘keyMappingType’ is defined then class ‘keyMappingType’ property will overwrite Parent level ‘keyMappingType’ value.

Returns:The key_mapping_type of this InputClass. Return type:str

name

Gets the name of this InputClass.

Returns:The name of this InputClass. Return type:str

primary_schema

Gets the primary_schema of this InputClass. If class mapped with more than one schemas then user can define either one of them as primary schema.

Returns:The primary_schema of this InputClass. Return type:str

to_dict()

Returns the model properties as a dict

to_str()

Returns the string representation of the model

sdi.models.input_class_property module

SDI - Semantic Data Interconnect APIs

The Semantic Data Interconnect (SDI) is a collection of APIs that allows the user to unlock the potential of disparate big data by connecting external data. The SDI can infer the schemas of data based on schema-on-read, allow creating a semantic model and perform big data semantic queries. It seamlessly connects to MindSphere’s Integrated Data Lake (IDL), but it can work independently as well. There are two mechanisms that can be used to upload files so that SDI can generate schemas and make data ready for query. The SDI operations are divided into the following groups: Data Registration for SDI This set of APIs is used to organize the incoming data. When configuring a Data Registry, you have the option to update your data based on a replace or append strategy. If you consider a use case where schema may change and incoming data files are completely different every time then replace is a good strategy. The replace strategy will replace the existing schema and data during each data ingest operation whereas the append strategy will update the existing schema and data during each data ingest operation. Custom Data Type for SDI The SDI by default identifies basic data types for each property, such as String, Integer, Float, Date, etc. The user can use this set of APIs to create their own custom data type. The SDI also provides an API under this category to suggest data type based on user-provided sample test values. Data Lake for SDI The SDI can process files uploaded provides endpoints to manage customer’s data lake registration based on tenant id, cloud provider and data lake type. The set of REST endpoint allows to create, update and retrieve base path for their data lake. The IDL customer needs to create an SDI folder that is under the root folder. Any file uploaded in this folder is automatically picked up by SDI to process via IDL notification. Data Ingest for SDI This set of APIs allows user to upload files, start an ingest job for uploaded files, find job status for ingested jobs or retrieve all job statuses. Schema Registry for SDI The SDI provides a way to find the generated schema in this category. Users can find an SDI generated schema for uploaded files based on source name, data tag or schema name. Data Query for SDI allows querying based on the extracted schemas. Important supported APIs are: * Query interface for querying semantically correlated and transformed data. * Stores and executes data queries. * Uses a semantic model to translate model-based query to physical queries. Semantic Model for SDI allows user to create semantic model ontologies based on the extracted one or more schemas. The important functionalities achieved with APIs are: * Contextual correlation of data from different systems. * Infers & Recommends mappings between different schemas. * Import and store Semantic model. # noqa: E501

class InputClassProperty(datatype=None, description=None, name=None, parent_class=None)

Bases: object

Attributes:

attribute_types (dict): The key is attribute name

and the value is attribute type.

attribute_map (dict): The key is attribute name

and the value is json key in definition.

attribute_map = {'datatype': 'datatype', 'description': 'description', 'name': 'name', 'parent_class': 'parentClass'}

attribute_types = {'datatype': 'str', 'description': 'str', 'name': 'str', 'parent_class': 'InputParent'}

datatype

Gets the datatype of this InputClassProperty.

Returns:The datatype of this InputClassProperty. Return type:str

description

Gets the description of this InputClassProperty.

Returns:The description of this InputClassProperty. Return type:str

name

Gets the name of this InputClassProperty.

Returns:The name of this InputClassProperty. Return type:str

parent_class

Gets the parent_class of this InputClassProperty.

Returns:The parent_class of this InputClassProperty. Return type:InputParent

to_dict()

Returns the model properties as a dict

to_str()

Returns the string representation of the model

sdi.models.input_mapping module

SDI - Semantic Data Interconnect APIs

The Semantic Data Interconnect (SDI) is a collection of APIs that allows the user to unlock the potential of disparate big data by connecting external data. The SDI can infer the schemas of data based on schema-on-read, allow creating a semantic model and perform big data semantic queries. It seamlessly connects to MindSphere’s Integrated Data Lake (IDL), but it can work independently as well. There are two mechanisms that can be used to upload files so that SDI can generate schemas and make data ready for query. The SDI operations are divided into the following groups: Data Registration for SDI This set of APIs is used to organize the incoming data. When configuring a Data Registry, you have the option to update your data based on a replace or append strategy. If you consider a use case where schema may change and incoming data files are completely different every time then replace is a good strategy. The replace strategy will replace the existing schema and data during each data ingest operation whereas the append strategy will update the existing schema and data during each data ingest operation. Custom Data Type for SDI The SDI by default identifies basic data types for each property, such as String, Integer, Float, Date, etc. The user can use this set of APIs to create their own custom data type. The SDI also provides an API under this category to suggest data type based on user-provided sample test values. Data Lake for SDI The SDI can process files uploaded provides endpoints to manage customer’s data lake registration based on tenant id, cloud provider and data lake type. The set of REST endpoint allows to create, update and retrieve base path for their data lake. The IDL customer needs to create an SDI folder that is under the root folder. Any file uploaded in this folder is automatically picked up by SDI to process via IDL notification. Data Ingest for SDI This set of APIs allows user to upload files, start an ingest job for uploaded files, find job status for ingested jobs or retrieve all job statuses. Schema Registry for SDI The SDI provides a way to find the generated schema in this category. Users can find an SDI generated schema for uploaded files based on source name, data tag or schema name. Data Query for SDI allows querying based on the extracted schemas. Important supported APIs are: * Query interface for querying semantically correlated and transformed data. * Stores and executes data queries. * Uses a semantic model to translate model-based query to physical queries. Semantic Model for SDI allows user to create semantic model ontologies based on the extracted one or more schemas. The important functionalities achieved with APIs are: * Contextual correlation of data from different systems. * Infers & Recommends mappings between different schemas. * Import and store Semantic model. # noqa: E501

class InputMapping(class_property=None, description=None, functional_mapping=None, key_mapping=None, mapping_function=None, name=None, schema_properties=None)

Bases: object

Attributes:

attribute_types (dict): The key is attribute name

and the value is attribute type.

attribute_map (dict): The key is attribute name

and the value is json key in definition.

attribute_map = {'class_property': 'classProperty', 'description': 'description', 'functional_mapping': 'functionalMapping', 'key_mapping': 'keyMapping', 'mapping_function': 'mappingFunction', 'name': 'name', 'schema_properties': 'schemaProperties'}

attribute_types = {'class_property': 'InputMappingClassProperty', 'description': 'str', 'functional_mapping': 'bool', 'key_mapping': 'bool', 'mapping_function': 'MappingFunction', 'name': 'str', 'schema_properties': 'list[InputMappingSchemaProperty]'}

class_property

Gets the class_property of this InputMapping.

Returns:The class_property of this InputMapping. Return type:InputMappingClassProperty

description

Gets the description of this InputMapping.

Returns:The description of this InputMapping. Return type:str

functional_mapping

Gets the functional_mapping of this InputMapping.

Returns:The functional_mapping of this InputMapping. Return type:bool

key_mapping

Gets the key_mapping of this InputMapping.

Returns:The key_mapping of this InputMapping. Return type:bool

mapping_function

Gets the mapping_function of this InputMapping.

Returns:The mapping_function of this InputMapping. Return type:MappingFunction

name

Gets the name of this InputMapping.

Returns:The name of this InputMapping. Return type:str

schema_properties

Gets the schema_properties of this InputMapping.

Returns:The schema_properties of this InputMapping. Return type:list[InputMappingSchemaProperty]

to_dict()

Returns the model properties as a dict

to_str()

Returns the string representation of the model

sdi.models.input_mapping_class_property module

SDI - Semantic Data Interconnect APIs

The Semantic Data Interconnect (SDI) is a collection of APIs that allows the user to unlock the potential of disparate big data by connecting external data. The SDI can infer the schemas of data based on schema-on-read, allow creating a semantic model and perform big data semantic queries. It seamlessly connects to MindSphere’s Integrated Data Lake (IDL), but it can work independently as well. There are two mechanisms that can be used to upload files so that SDI can generate schemas and make data ready for query. The SDI operations are divided into the following groups: Data Registration for SDI This set of APIs is used to organize the incoming data. When configuring a Data Registry, you have the option to update your data based on a replace or append strategy. If you consider a use case where schema may change and incoming data files are completely different every time then replace is a good strategy. The replace strategy will replace the existing schema and data during each data ingest operation whereas the append strategy will update the existing schema and data during each data ingest operation. Custom Data Type for SDI The SDI by default identifies basic data types for each property, such as String, Integer, Float, Date, etc. The user can use this set of APIs to create their own custom data type. The SDI also provides an API under this category to suggest data type based on user-provided sample test values. Data Lake for SDI The SDI can process files uploaded provides endpoints to manage customer’s data lake registration based on tenant id, cloud provider and data lake type. The set of REST endpoint allows to create, update and retrieve base path for their data lake. The IDL customer needs to create an SDI folder that is under the root folder. Any file uploaded in this folder is automatically picked up by SDI to process via IDL notification. Data Ingest for SDI This set of APIs allows user to upload files, start an ingest job for uploaded files, find job status for ingested jobs or retrieve all job statuses. Schema Registry for SDI The SDI provides a way to find the generated schema in this category. Users can find an SDI generated schema for uploaded files based on source name, data tag or schema name. Data Query for SDI allows querying based on the extracted schemas. Important supported APIs are: * Query interface for querying semantically correlated and transformed data. * Stores and executes data queries. * Uses a semantic model to translate model-based query to physical queries. Semantic Model for SDI allows user to create semantic model ontologies based on the extracted one or more schemas. The important functionalities achieved with APIs are: * Contextual correlation of data from different systems. * Infers & Recommends mappings between different schemas. * Import and store Semantic model. # noqa: E501

class InputMappingClassProperty(name=None, parent_class=None)

Bases: object

Attributes:

attribute_types (dict): The key is attribute name

and the value is attribute type.

attribute_map (dict): The key is attribute name

and the value is json key in definition.

attribute_map = {'name': 'name', 'parent_class': 'parentClass'}

attribute_types = {'name': 'str', 'parent_class': 'InputParent'}

name

Gets the name of this InputMappingClassProperty.

Returns:The name of this InputMappingClassProperty. Return type:str

parent_class

Gets the parent_class of this InputMappingClassProperty.

Returns:The parent_class of this InputMappingClassProperty. Return type:InputParent

to_dict()

Returns the model properties as a dict

to_str()

Returns the string representation of the model

sdi.models.input_mapping_schema_property module

SDI - Semantic Data Interconnect APIs

The Semantic Data Interconnect (SDI) is a collection of APIs that allows the user to unlock the potential of disparate big data by connecting external data. The SDI can infer the schemas of data based on schema-on-read, allow creating a semantic model and perform big data semantic queries. It seamlessly connects to MindSphere’s Integrated Data Lake (IDL), but it can work independently as well. There are two mechanisms that can be used to upload files so that SDI can generate schemas and make data ready for query. The SDI operations are divided into the following groups: Data Registration for SDI This set of APIs is used to organize the incoming data. When configuring a Data Registry, you have the option to update your data based on a replace or append strategy. If you consider a use case where schema may change and incoming data files are completely different every time then replace is a good strategy. The replace strategy will replace the existing schema and data during each data ingest operation whereas the append strategy will update the existing schema and data during each data ingest operation. Custom Data Type for SDI The SDI by default identifies basic data types for each property, such as String, Integer, Float, Date, etc. The user can use this set of APIs to create their own custom data type. The SDI also provides an API under this category to suggest data type based on user-provided sample test values. Data Lake for SDI The SDI can process files uploaded provides endpoints to manage customer’s data lake registration based on tenant id, cloud provider and data lake type. The set of REST endpoint allows to create, update and retrieve base path for their data lake. The IDL customer needs to create an SDI folder that is under the root folder. Any file uploaded in this folder is automatically picked up by SDI to process via IDL notification. Data Ingest for SDI This set of APIs allows user to upload files, start an ingest job for uploaded files, find job status for ingested jobs or retrieve all job statuses. Schema Registry for SDI The SDI provides a way to find the generated schema in this category. Users can find an SDI generated schema for uploaded files based on source name, data tag or schema name. Data Query for SDI allows querying based on the extracted schemas. Important supported APIs are: * Query interface for querying semantically correlated and transformed data. * Stores and executes data queries. * Uses a semantic model to translate model-based query to physical queries. Semantic Model for SDI allows user to create semantic model ontologies based on the extracted one or more schemas. The important functionalities achieved with APIs are: * Contextual correlation of data from different systems. * Infers & Recommends mappings between different schemas. * Import and store Semantic model. # noqa: E501

class InputMappingSchemaProperty(name=None, order=None, parent_schema=None)

Bases: object

Attributes:

attribute_types (dict): The key is attribute name

and the value is attribute type.

attribute_map (dict): The key is attribute name

and the value is json key in definition.

attribute_map = {'name': 'name', 'order': 'order', 'parent_schema': 'parentSchema'}

attribute_types = {'name': 'str', 'order': 'str', 'parent_schema': 'InputParent'}

name

Gets the name of this InputMappingSchemaProperty.

Returns:The name of this InputMappingSchemaProperty. Return type:str

order

Gets the order of this InputMappingSchemaProperty.

Returns:The order of this InputMappingSchemaProperty. Return type:str

parent_schema

Gets the parent_schema of this InputMappingSchemaProperty.

Returns:The parent_schema of this InputMappingSchemaProperty. Return type:InputParent

to_dict()

Returns the model properties as a dict

to_str()

Returns the string representation of the model

sdi.models.input_parent module

SDI - Semantic Data Interconnect APIs

The Semantic Data Interconnect (SDI) is a collection of APIs that allows the user to unlock the potential of disparate big data by connecting external data. The SDI can infer the schemas of data based on schema-on-read, allow creating a semantic model and perform big data semantic queries. It seamlessly connects to MindSphere’s Integrated Data Lake (IDL), but it can work independently as well. There are two mechanisms that can be used to upload files so that SDI can generate schemas and make data ready for query. The SDI operations are divided into the following groups: Data Registration for SDI This set of APIs is used to organize the incoming data. When configuring a Data Registry, you have the option to update your data based on a replace or append strategy. If you consider a use case where schema may change and incoming data files are completely different every time then replace is a good strategy. The replace strategy will replace the existing schema and data during each data ingest operation whereas the append strategy will update the existing schema and data during each data ingest operation. Custom Data Type for SDI The SDI by default identifies basic data types for each property, such as String, Integer, Float, Date, etc. The user can use this set of APIs to create their own custom data type. The SDI also provides an API under this category to suggest data type based on user-provided sample test values. Data Lake for SDI The SDI can process files uploaded provides endpoints to manage customer’s data lake registration based on tenant id, cloud provider and data lake type. The set of REST endpoint allows to create, update and retrieve base path for their data lake. The IDL customer needs to create an SDI folder that is under the root folder. Any file uploaded in this folder is automatically picked up by SDI to process via IDL notification. Data Ingest for SDI This set of APIs allows user to upload files, start an ingest job for uploaded files, find job status for ingested jobs or retrieve all job statuses. Schema Registry for SDI The SDI provides a way to find the generated schema in this category. Users can find an SDI generated schema for uploaded files based on source name, data tag or schema name. Data Query for SDI allows querying based on the extracted schemas. Important supported APIs are: * Query interface for querying semantically correlated and transformed data. * Stores and executes data queries. * Uses a semantic model to translate model-based query to physical queries. Semantic Model for SDI allows user to create semantic model ontologies based on the extracted one or more schemas. The important functionalities achieved with APIs are: * Contextual correlation of data from different systems. * Infers & Recommends mappings between different schemas. * Import and store Semantic model. # noqa: E501

class InputParent(name=None)

Bases: object

Attributes:

attribute_types (dict): The key is attribute name

and the value is attribute type.

attribute_map (dict): The key is attribute name

and the value is json key in definition.

attribute_map = {'name': 'name'}

attribute_types = {'name': 'str'}

name

Gets the name of this InputParent.

Returns:The name of this InputParent. Return type:str

to_dict()

Returns the model properties as a dict

to_str()

Returns the string representation of the model

sdi.models.input_property_relation module

SDI - Semantic Data Interconnect APIs

The Semantic Data Interconnect (SDI) is a collection of APIs that allows the user to unlock the potential of disparate big data by connecting external data. The SDI can infer the schemas of data based on schema-on-read, allow creating a semantic model and perform big data semantic queries. It seamlessly connects to MindSphere’s Integrated Data Lake (IDL), but it can work independently as well. There are two mechanisms that can be used to upload files so that SDI can generate schemas and make data ready for query. The SDI operations are divided into the following groups: Data Registration for SDI This set of APIs is used to organize the incoming data. When configuring a Data Registry, you have the option to update your data based on a replace or append strategy. If you consider a use case where schema may change and incoming data files are completely different every time then replace is a good strategy. The replace strategy will replace the existing schema and data during each data ingest operation whereas the append strategy will update the existing schema and data during each data ingest operation. Custom Data Type for SDI The SDI by default identifies basic data types for each property, such as String, Integer, Float, Date, etc. The user can use this set of APIs to create their own custom data type. The SDI also provides an API under this category to suggest data type based on user-provided sample test values. Data Lake for SDI The SDI can process files uploaded provides endpoints to manage customer’s data lake registration based on tenant id, cloud provider and data lake type. The set of REST endpoint allows to create, update and retrieve base path for their data lake. The IDL customer needs to create an SDI folder that is under the root folder. Any file uploaded in this folder is automatically picked up by SDI to process via IDL notification. Data Ingest for SDI This set of APIs allows user to upload files, start an ingest job for uploaded files, find job status for ingested jobs or retrieve all job statuses. Schema Registry for SDI The SDI provides a way to find the generated schema in this category. Users can find an SDI generated schema for uploaded files based on source name, data tag or schema name. Data Query for SDI allows querying based on the extracted schemas. Important supported APIs are: * Query interface for querying semantically correlated and transformed data. * Stores and executes data queries. * Uses a semantic model to translate model-based query to physical queries. Semantic Model for SDI allows user to create semantic model ontologies based on the extracted one or more schemas. The important functionalities achieved with APIs are: * Contextual correlation of data from different systems. * Infers & Recommends mappings between different schemas. * Import and store Semantic model. # noqa: E501

class InputPropertyRelation(description=None, end_class_property=None, name=None, relation_type=None, start_class_property=None)

Bases: object

Attributes:

attribute_types (dict): The key is attribute name

and the value is attribute type.

attribute_map (dict): The key is attribute name

and the value is json key in definition.

attribute_map = {'description': 'description', 'end_class_property': 'endClassProperty', 'name': 'name', 'relation_type': 'relationType', 'start_class_property': 'startClassProperty'}

attribute_types = {'description': 'str', 'end_class_property': 'InputMappingClassProperty', 'name': 'str', 'relation_type': 'str', 'start_class_property': 'InputMappingClassProperty'}

description

Gets the description of this InputPropertyRelation.

Returns:The description of this InputPropertyRelation. Return type:str

end_class_property

Gets the end_class_property of this InputPropertyRelation.

Returns:The end_class_property of this InputPropertyRelation. Return type:InputMappingClassProperty

name

Gets the name of this InputPropertyRelation.

Returns:The name of this InputPropertyRelation. Return type:str

relation_type

Gets the relation_type of this InputPropertyRelation.

Returns:The relation_type of this InputPropertyRelation. Return type:str

start_class_property

Gets the start_class_property of this InputPropertyRelation.

Returns:The start_class_property of this InputPropertyRelation. Return type:InputMappingClassProperty

to_dict()

Returns the model properties as a dict

to_str()

Returns the string representation of the model

sdi.models.input_schema module

SDI - Semantic Data Interconnect APIs

The Semantic Data Interconnect (SDI) is a collection of APIs that allows the user to unlock the potential of disparate big data by connecting external data. The SDI can infer the schemas of data based on schema-on-read, allow creating a semantic model and perform big data semantic queries. It seamlessly connects to MindSphere’s Integrated Data Lake (IDL), but it can work independently as well. There are two mechanisms that can be used to upload files so that SDI can generate schemas and make data ready for query. The SDI operations are divided into the following groups: Data Registration for SDI This set of APIs is used to organize the incoming data. When configuring a Data Registry, you have the option to update your data based on a replace or append strategy. If you consider a use case where schema may change and incoming data files are completely different every time then replace is a good strategy. The replace strategy will replace the existing schema and data during each data ingest operation whereas the append strategy will update the existing schema and data during each data ingest operation. Custom Data Type for SDI The SDI by default identifies basic data types for each property, such as String, Integer, Float, Date, etc. The user can use this set of APIs to create their own custom data type. The SDI also provides an API under this category to suggest data type based on user-provided sample test values. Data Lake for SDI The SDI can process files uploaded provides endpoints to manage customer’s data lake registration based on tenant id, cloud provider and data lake type. The set of REST endpoint allows to create, update and retrieve base path for their data lake. The IDL customer needs to create an SDI folder that is under the root folder. Any file uploaded in this folder is automatically picked up by SDI to process via IDL notification. Data Ingest for SDI This set of APIs allows user to upload files, start an ingest job for uploaded files, find job status for ingested jobs or retrieve all job statuses. Schema Registry for SDI The SDI provides a way to find the generated schema in this category. Users can find an SDI generated schema for uploaded files based on source name, data tag or schema name. Data Query for SDI allows querying based on the extracted schemas. Important supported APIs are: * Query interface for querying semantically correlated and transformed data. * Stores and executes data queries. * Uses a semantic model to translate model-based query to physical queries. Semantic Model for SDI allows user to create semantic model ontologies based on the extracted one or more schemas. The important functionalities achieved with APIs are: * Contextual correlation of data from different systems. * Infers & Recommends mappings between different schemas. * Import and store Semantic model. # noqa: E501

class InputSchema(description=None, name=None)

Bases: object

Attributes:

attribute_types (dict): The key is attribute name

and the value is attribute type.

attribute_map (dict): The key is attribute name

and the value is json key in definition.

attribute_map = {'description': 'description', 'name': 'name'}

attribute_types = {'description': 'str', 'name': 'str'}

description

Gets the description of this InputSchema.

Returns:The description of this InputSchema. Return type:str

name

Gets the name of this InputSchema.

Returns:The name of this InputSchema. Return type:str

to_dict()

Returns the model properties as a dict

to_str()

Returns the string representation of the model

sdi.models.input_schema_property module

SDI - Semantic Data Interconnect APIs

The Semantic Data Interconnect (SDI) is a collection of APIs that allows the user to unlock the potential of disparate big data by connecting external data. The SDI can infer the schemas of data based on schema-on-read, allow creating a semantic model and perform big data semantic queries. It seamlessly connects to MindSphere’s Integrated Data Lake (IDL), but it can work independently as well. There are two mechanisms that can be used to upload files so that SDI can generate schemas and make data ready for query. The SDI operations are divided into the following groups: Data Registration for SDI This set of APIs is used to organize the incoming data. When configuring a Data Registry, you have the option to update your data based on a replace or append strategy. If you consider a use case where schema may change and incoming data files are completely different every time then replace is a good strategy. The replace strategy will replace the existing schema and data during each data ingest operation whereas the append strategy will update the existing schema and data during each data ingest operation. Custom Data Type for SDI The SDI by default identifies basic data types for each property, such as String, Integer, Float, Date, etc. The user can use this set of APIs to create their own custom data type. The SDI also provides an API under this category to suggest data type based on user-provided sample test values. Data Lake for SDI The SDI can process files uploaded provides endpoints to manage customer’s data lake registration based on tenant id, cloud provider and data lake type. The set of REST endpoint allows to create, update and retrieve base path for their data lake. The IDL customer needs to create an SDI folder that is under the root folder. Any file uploaded in this folder is automatically picked up by SDI to process via IDL notification. Data Ingest for SDI This set of APIs allows user to upload files, start an ingest job for uploaded files, find job status for ingested jobs or retrieve all job statuses. Schema Registry for SDI The SDI provides a way to find the generated schema in this category. Users can find an SDI generated schema for uploaded files based on source name, data tag or schema name. Data Query for SDI allows querying based on the extracted schemas. Important supported APIs are: * Query interface for querying semantically correlated and transformed data. * Stores and executes data queries. * Uses a semantic model to translate model-based query to physical queries. Semantic Model for SDI allows user to create semantic model ontologies based on the extracted one or more schemas. The important functionalities achieved with APIs are: * Contextual correlation of data from different systems. * Infers & Recommends mappings between different schemas. * Import and store Semantic model. # noqa: E501

class InputSchemaProperty(datatype=None, description=None, name=None, parent_schema=None)

Bases: object

Attributes:

attribute_types (dict): The key is attribute name

and the value is attribute type.

attribute_map (dict): The key is attribute name

and the value is json key in definition.

attribute_map = {'datatype': 'datatype', 'description': 'description', 'name': 'name', 'parent_schema': 'parentSchema'}

attribute_types = {'datatype': 'str', 'description': 'str', 'name': 'str', 'parent_schema': 'InputParent'}

datatype

Gets the datatype of this InputSchemaProperty.

Returns:The datatype of this InputSchemaProperty. Return type:str

description

Gets the description of this InputSchemaProperty.

Returns:The description of this InputSchemaProperty. Return type:str

name

Gets the name of this InputSchemaProperty.

Returns:The name of this InputSchemaProperty. Return type:str

parent_schema

Gets the parent_schema of this InputSchemaProperty.

Returns:The parent_schema of this InputSchemaProperty. Return type:InputParent

to_dict()

Returns the model properties as a dict

to_str()

Returns the string representation of the model

sdi.models.iot_data_registry module

SDI - Semantic Data Interconnect APIs

The Semantic Data Interconnect (SDI) is a collection of APIs that allows the user to unlock the potential of disparate big data by connecting external data. The SDI can infer the schemas of data based on schema-on-read, allow creating a semantic model and perform big data semantic queries. It seamlessly connects to MindSphere’s Integrated Data Lake (IDL), but it can work independently as well. There are two mechanisms that can be used to upload files so that SDI can generate schemas and make data ready for query. The SDI operations are divided into the following groups: Data Registration for SDI This set of APIs is used to organize the incoming data. When configuring a Data Registry, you have the option to update your data based on a replace or append strategy. If you consider a use case where schema may change and incoming data files are completely different every time then replace is a good strategy. The replace strategy will replace the existing schema and data during each data ingest operation whereas the append strategy will update the existing schema and data during each data ingest operation. Custom Data Type for SDI The SDI by default identifies basic data types for each property, such as String, Integer, Float, Date, etc. The user can use this set of APIs to create their own custom data type. The SDI also provides an API under this category to suggest data type based on user-provided sample test values. Data Lake for SDI The SDI can process files uploaded provides endpoints to manage customer’s data lake registration based on tenant id, cloud provider and data lake type. The set of REST endpoint allows to create, update and retrieve base path for their data lake. The IDL customer needs to create an SDI folder that is under the root folder. Any file uploaded in this folder is automatically picked up by SDI to process via IDL notification. Data Ingest for SDI This set of APIs allows user to upload files, start an ingest job for uploaded files, find job status for ingested jobs or retrieve all job statuses. Schema Registry for SDI The SDI provides a way to find the generated schema in this category. Users can find an SDI generated schema for uploaded files based on source name, data tag or schema name. Data Query for SDI allows querying based on the extracted schemas. Important supported APIs are: * Query interface for querying semantically correlated and transformed data. * Stores and executes data queries. * Uses a semantic model to translate model-based query to physical queries. Semantic Model for SDI allows user to create semantic model ontologies based on the extracted one or more schemas. The important functionalities achieved with APIs are: * Contextual correlation of data from different systems. * Infers & Recommends mappings between different schemas. * Import and store Semantic model. # noqa: E501

class IotDataRegistry(asset_id=None, aspect_name=None)

Bases: object

Attributes:

attribute_types (dict): The key is attribute name

and the value is attribute type.

attribute_map (dict): The key is attribute name

and the value is json key in definition.

aspect_name

Gets the aspect_name of this IotDataRegistry.

Returns:The aspect_name of this IotDataRegistry. Return type:str

asset_id

Gets the asset_id of this IotDataRegistry.

Returns:The asset_id of this IotDataRegistry. Return type:str

attribute_map = {'aspect_name': 'aspectName', 'asset_id': 'assetId'}

attribute_types = {'aspect_name': 'str', 'asset_id': 'str'}

to_dict()

Returns the model properties as a dict

to_str()

Returns the string representation of the model

sdi.models.iot_data_registry_response module

SDI - Semantic Data Interconnect APIs

The Semantic Data Interconnect (SDI) is a collection of APIs that allows the user to unlock the potential of disparate big data by connecting external data. The SDI can infer the schemas of data based on schema-on-read, allow creating a semantic model and perform big data semantic queries. It seamlessly connects to MindSphere’s Integrated Data Lake (IDL), but it can work independently as well. There are two mechanisms that can be used to upload files so that SDI can generate schemas and make data ready for query. The SDI operations are divided into the following groups: Data Registration for SDI This set of APIs is used to organize the incoming data. When configuring a Data Registry, you have the option to update your data based on a replace or append strategy. If you consider a use case where schema may change and incoming data files are completely different every time then replace is a good strategy. The replace strategy will replace the existing schema and data during each data ingest operation whereas the append strategy will update the existing schema and data during each data ingest operation. Custom Data Type for SDI The SDI by default identifies basic data types for each property, such as String, Integer, Float, Date, etc. The user can use this set of APIs to create their own custom data type. The SDI also provides an API under this category to suggest data type based on user-provided sample test values. Data Lake for SDI The SDI can process files uploaded provides endpoints to manage customer’s data lake registration based on tenant id, cloud provider and data lake type. The set of REST endpoint allows to create, update and retrieve base path for their data lake. The IDL customer needs to create an SDI folder that is under the root folder. Any file uploaded in this folder is automatically picked up by SDI to process via IDL notification. Data Ingest for SDI This set of APIs allows user to upload files, start an ingest job for uploaded files, find job status for ingested jobs or retrieve all job statuses. Schema Registry for SDI The SDI provides a way to find the generated schema in this category. Users can find an SDI generated schema for uploaded files based on source name, data tag or schema name. Data Query for SDI allows querying based on the extracted schemas. Important supported APIs are: * Query interface for querying semantically correlated and transformed data. * Stores and executes data queries. * Uses a semantic model to translate model-based query to physical queries. Semantic Model for SDI allows user to create semantic model ontologies based on the extracted one or more schemas. The important functionalities achieved with APIs are: * Contextual correlation of data from different systems. * Infers & Recommends mappings between different schemas. * Import and store Semantic model. # noqa: E501

class IotDataRegistryResponse(aspect_name=None, asset_id=None, category=None, created_date=None, data_tag=None, file_upload_strategy=None, updated_date=None, registry_id=None, source_name=None)

Bases: object

Attributes:

attribute_types (dict): The key is attribute name

and the value is attribute type.

attribute_map (dict): The key is attribute name

and the value is json key in definition.

aspect_name

Gets the aspect_name of this IotDataRegistryResponse.

Returns:The aspect_name of this IotDataRegistryResponse. Return type:str

asset_id

Gets the asset_id of this IotDataRegistryResponse.

Returns:The asset_id of this IotDataRegistryResponse. Return type:str

attribute_map = {'aspect_name': 'aspectName', 'asset_id': 'assetId', 'category': 'category', 'created_date': 'createdDate', 'data_tag': 'dataTag', 'file_upload_strategy': 'fileUploadStrategy', 'registry_id': 'registryId', 'source_name': 'sourceName', 'updated_date': 'updatedDate'}

attribute_types = {'aspect_name': 'str', 'asset_id': 'str', 'category': 'str', 'created_date': 'str', 'data_tag': 'str', 'file_upload_strategy': 'str', 'registry_id': 'str', 'source_name': 'str', 'updated_date': 'str'}

category

Gets the category of this IotDataRegistryResponse. The category for this IoT Data Registry is always IOT

Returns:The category of this IotDataRegistryResponse. Return type:str

created_date

Gets the created_date of this IotDataRegistryResponse.

Returns:The created_date of this IotDataRegistryResponse. Return type:str

data_tag

Gets the data_tag of this IotDataRegistryResponse. The dataTag is combination of assetId and aspectName, separated by _

Returns:The data_tag of this IotDataRegistryResponse. Return type:str

file_upload_strategy

Gets the file_upload_strategy of this IotDataRegistryResponse.

Returns:The file_upload_strategy of this IotDataRegistryResponse. Return type:str

registry_id

Gets the registry_id of this IotDataRegistryResponse.

Returns:The registry_id of this IotDataRegistryResponse. Return type:str

source_name

Gets the source_name of this IotDataRegistryResponse. The sourceName is always MindSphere.

Returns:The source_name of this IotDataRegistryResponse. Return type:str

to_dict()

Returns the model properties as a dict

to_str()

Returns the string representation of the model

updated_date

Gets the updated_date of this IotDataRegistryResponse.

Returns:The updated_date of this IotDataRegistryResponse. Return type:str

sdi.models.job_status module

SDI - Semantic Data Interconnect APIs

The Semantic Data Interconnect (SDI) is a collection of APIs that allows the user to unlock the potential of disparate big data by connecting external data. The SDI can infer the schemas of data based on schema-on-read, allow creating a semantic model and perform big data semantic queries. It seamlessly connects to MindSphere’s Integrated Data Lake (IDL), but it can work independently as well. There are two mechanisms that can be used to upload files so that SDI can generate schemas and make data ready for query. The SDI operations are divided into the following groups: Data Registration for SDI This set of APIs is used to organize the incoming data. When configuring a Data Registry, you have the option to update your data based on a replace or append strategy. If you consider a use case where schema may change and incoming data files are completely different every time then replace is a good strategy. The replace strategy will replace the existing schema and data during each data ingest operation whereas the append strategy will update the existing schema and data during each data ingest operation. Custom Data Type for SDI The SDI by default identifies basic data types for each property, such as String, Integer, Float, Date, etc. The user can use this set of APIs to create their own custom data type. The SDI also provides an API under this category to suggest data type based on user-provided sample test values. Data Lake for SDI The SDI can process files uploaded provides endpoints to manage customer’s data lake registration based on tenant id, cloud provider and data lake type. The set of REST endpoint allows to create, update and retrieve base path for their data lake. The IDL customer needs to create an SDI folder that is under the root folder. Any file uploaded in this folder is automatically picked up by SDI to process via IDL notification. Data Ingest for SDI This set of APIs allows user to upload files, start an ingest job for uploaded files, find job status for ingested jobs or retrieve all job statuses. Schema Registry for SDI The SDI provides a way to find the generated schema in this category. Users can find an SDI generated schema for uploaded files based on source name, data tag or schema name. Data Query for SDI allows querying based on the extracted schemas. Important supported APIs are: * Query interface for querying semantically correlated and transformed data. * Stores and executes data queries. * Uses a semantic model to translate model-based query to physical queries. Semantic Model for SDI allows user to create semantic model ontologies based on the extracted one or more schemas. The important functionalities achieved with APIs are: * Contextual correlation of data from different systems. * Infers & Recommends mappings between different schemas. * Import and store Semantic model. # noqa: E501

class JobStatus(id=None, status=None, message=None, created_date=None, updated_date=None, ontology_response=None)

Bases: object

Attributes:

attribute_types (dict): The key is attribute name

and the value is attribute type.

attribute_map (dict): The key is attribute name

and the value is json key in definition.

attribute_map = {'created_date': 'createdDate', 'id': 'id', 'message': 'message', 'ontology_response': 'ontologyResponse', 'status': 'status', 'updated_date': 'updatedDate'}

attribute_types = {'created_date': 'str', 'id': 'str', 'message': 'str', 'ontology_response': 'JobStatusOntologyResponse', 'status': 'str', 'updated_date': 'str'}

created_date

Gets the created_date of this JobStatus. Start time of Ontology job created in UTC date format.

Returns:The created_date of this JobStatus. Return type:str

id

Gets the id of this JobStatus. Unique Ontology job ID.

Returns:The id of this JobStatus. Return type:str

message

Gets the message of this JobStatus. Contains an message in case the job created. Possible messages: - The Request for Create Ontology. - The Request for Create Ontology using owl file upload. - The Request for Update Ontology.

Returns:The message of this JobStatus. Return type:str

ontology_response

Gets the ontology_response of this JobStatus.

Returns:The ontology_response of this JobStatus. Return type:JobStatusOntologyResponse

status

Gets the status of this JobStatus. Status of ontology creation/updation job. - SUBMITTED: job has been created but creation/updation of ontology not yet started. - IN_PROGRESS: Ontology creation or updation started. - FAILED: Ontology creation or updation has failed. No data is available to be retrieved. - SUCCESS: Ontology creation or updation has been successfully finished.

Returns:The status of this JobStatus. Return type:str

to_dict()

Returns the model properties as a dict

to_str()

Returns the string representation of the model

updated_date

Gets the updated_date of this JobStatus. Job last modified time in UTC date format. The backend updates this time whenever the job status changes.

Returns:The updated_date of this JobStatus. Return type:str

sdi.models.job_status_ontology_response module

SDI - Semantic Data Interconnect APIs

The Semantic Data Interconnect (SDI) is a collection of APIs that allows the user to unlock the potential of disparate big data by connecting external data. The SDI can infer the schemas of data based on schema-on-read, allow creating a semantic model and perform big data semantic queries. It seamlessly connects to MindSphere’s Integrated Data Lake (IDL), but it can work independently as well. There are two mechanisms that can be used to upload files so that SDI can generate schemas and make data ready for query. The SDI operations are divided into the following groups: Data Registration for SDI This set of APIs is used to organize the incoming data. When configuring a Data Registry, you have the option to update your data based on a replace or append strategy. If you consider a use case where schema may change and incoming data files are completely different every time then replace is a good strategy. The replace strategy will replace the existing schema and data during each data ingest operation whereas the append strategy will update the existing schema and data during each data ingest operation. Custom Data Type for SDI The SDI by default identifies basic data types for each property, such as String, Integer, Float, Date, etc. The user can use this set of APIs to create their own custom data type. The SDI also provides an API under this category to suggest data type based on user-provided sample test values. Data Lake for SDI The SDI can process files uploaded provides endpoints to manage customer’s data lake registration based on tenant id, cloud provider and data lake type. The set of REST endpoint allows to create, update and retrieve base path for their data lake. The IDL customer needs to create an SDI folder that is under the root folder. Any file uploaded in this folder is automatically picked up by SDI to process via IDL notification. Data Ingest for SDI This set of APIs allows user to upload files, start an ingest job for uploaded files, find job status for ingested jobs or retrieve all job statuses. Schema Registry for SDI The SDI provides a way to find the generated schema in this category. Users can find an SDI generated schema for uploaded files based on source name, data tag or schema name. Data Query for SDI allows querying based on the extracted schemas. Important supported APIs are: * Query interface for querying semantically correlated and transformed data. * Stores and executes data queries. * Uses a semantic model to translate model-based query to physical queries. Semantic Model for SDI allows user to create semantic model ontologies based on the extracted one or more schemas. The important functionalities achieved with APIs are: * Contextual correlation of data from different systems. * Infers & Recommends mappings between different schemas. * Import and store Semantic model. # noqa: E501

class JobStatusOntologyResponse(ontology_id=None, ontology_errors=None)

Bases: object

Attributes:

attribute_types (dict): The key is attribute name

and the value is attribute type.

attribute_map (dict): The key is attribute name

and the value is json key in definition.

attribute_map = {'ontology_errors': 'ontologyErrors', 'ontology_id': 'ontologyId'}

attribute_types = {'ontology_errors': 'list[ApiFieldError]', 'ontology_id': 'str'}

ontology_errors

Gets the ontology_errors of this JobStatusOntologyResponse. ontologyErrors will be present if ontology creation failed.

Returns:The ontology_errors of this JobStatusOntologyResponse. Return type:list[ApiFieldError]

ontology_id

Gets the ontology_id of this JobStatusOntologyResponse. Ontology id will be present if ontology creation successful.

Returns:The ontology_id of this JobStatusOntologyResponse. Return type:str

to_dict()

Returns the model properties as a dict

to_str()

Returns the string representation of the model

sdi.models.list_of_data_type_definition module

SDI - Semantic Data Interconnect APIs

The Semantic Data Interconnect (SDI) is a collection of APIs that allows the user to unlock the potential of disparate big data by connecting external data. The SDI can infer the schemas of data based on schema-on-read, allow creating a semantic model and perform big data semantic queries. It seamlessly connects to MindSphere’s Integrated Data Lake (IDL), but it can work independently as well. There are two mechanisms that can be used to upload files so that SDI can generate schemas and make data ready for query. The SDI operations are divided into the following groups: Data Registration for SDI This set of APIs is used to organize the incoming data. When configuring a Data Registry, you have the option to update your data based on a replace or append strategy. If you consider a use case where schema may change and incoming data files are completely different every time then replace is a good strategy. The replace strategy will replace the existing schema and data during each data ingest operation whereas the append strategy will update the existing schema and data during each data ingest operation. Custom Data Type for SDI The SDI by default identifies basic data types for each property, such as String, Integer, Float, Date, etc. The user can use this set of APIs to create their own custom data type. The SDI also provides an API under this category to suggest data type based on user-provided sample test values. Data Lake for SDI The SDI can process files uploaded provides endpoints to manage customer’s data lake registration based on tenant id, cloud provider and data lake type. The set of REST endpoint allows to create, update and retrieve base path for their data lake. The IDL customer needs to create an SDI folder that is under the root folder. Any file uploaded in this folder is automatically picked up by SDI to process via IDL notification. Data Ingest for SDI This set of APIs allows user to upload files, start an ingest job for uploaded files, find job status for ingested jobs or retrieve all job statuses. Schema Registry for SDI The SDI provides a way to find the generated schema in this category. Users can find an SDI generated schema for uploaded files based on source name, data tag or schema name. Data Query for SDI allows querying based on the extracted schemas. Important supported APIs are: * Query interface for querying semantically correlated and transformed data. * Stores and executes data queries. * Uses a semantic model to translate model-based query to physical queries. Semantic Model for SDI allows user to create semantic model ontologies based on the extracted one or more schemas. The important functionalities achieved with APIs are: * Contextual correlation of data from different systems. * Infers & Recommends mappings between different schemas. * Import and store Semantic model. # noqa: E501

class ListOfDataTypeDefinition(data_types=None, page=None)

Bases: object

Attributes:

attribute_types (dict): The key is attribute name

and the value is attribute type.

attribute_map (dict): The key is attribute name

and the value is json key in definition.

attribute_map = {'data_types': 'dataTypes', 'page': 'page'}

attribute_types = {'data_types': 'list[DataTypeDefinition]', 'page': 'TokenPage'}

data_types

Gets the data_types of this ListOfDataTypeDefinition.

Returns:The data_types of this ListOfDataTypeDefinition. Return type:list[DataTypeDefinition]

page

Gets the page of this ListOfDataTypeDefinition.

Returns:The page of this ListOfDataTypeDefinition. Return type:TokenPage

to_dict()

Returns the model properties as a dict

to_str()

Returns the string representation of the model

sdi.models.list_of_io_t_registry_response module

SDI - Semantic Data Interconnect APIs

The Semantic Data Interconnect (SDI) is a collection of APIs that allows the user to unlock the potential of disparate big data by connecting external data. The SDI can infer the schemas of data based on schema-on-read, allow creating a semantic model and perform big data semantic queries. It seamlessly connects to MindSphere’s Integrated Data Lake (IDL), but it can work independently as well. There are two mechanisms that can be used to upload files so that SDI can generate schemas and make data ready for query. The SDI operations are divided into the following groups: Data Registration for SDI This set of APIs is used to organize the incoming data. When configuring a Data Registry, you have the option to update your data based on a replace or append strategy. If you consider a use case where schema may change and incoming data files are completely different every time then replace is a good strategy. The replace strategy will replace the existing schema and data during each data ingest operation whereas the append strategy will update the existing schema and data during each data ingest operation. Custom Data Type for SDI The SDI by default identifies basic data types for each property, such as String, Integer, Float, Date, etc. The user can use this set of APIs to create their own custom data type. The SDI also provides an API under this category to suggest data type based on user-provided sample test values. Data Lake for SDI The SDI can process files uploaded provides endpoints to manage customer’s data lake registration based on tenant id, cloud provider and data lake type. The set of REST endpoint allows to create, update and retrieve base path for their data lake. The IDL customer needs to create an SDI folder that is under the root folder. Any file uploaded in this folder is automatically picked up by SDI to process via IDL notification. Data Ingest for SDI This set of APIs allows user to upload files, start an ingest job for uploaded files, find job status for ingested jobs or retrieve all job statuses. Schema Registry for SDI The SDI provides a way to find the generated schema in this category. Users can find an SDI generated schema for uploaded files based on source name, data tag or schema name. Data Query for SDI allows querying based on the extracted schemas. Important supported APIs are: * Query interface for querying semantically correlated and transformed data. * Stores and executes data queries. * Uses a semantic model to translate model-based query to physical queries. Semantic Model for SDI allows user to create semantic model ontologies based on the extracted one or more schemas. The important functionalities achieved with APIs are: * Contextual correlation of data from different systems. * Infers & Recommends mappings between different schemas. * Import and store Semantic model. # noqa: E501

class ListOfIoTRegistryResponse(iot_data_registries=None, page=None)

Bases: object

Attributes:

attribute_types (dict): The key is attribute name

and the value is attribute type.

attribute_map (dict): The key is attribute name

and the value is json key in definition.

attribute_map = {'iot_data_registries': 'iotDataRegistries', 'page': 'page'}

attribute_types = {'iot_data_registries': 'list[IotDataRegistryResponse]', 'page': 'TokenPage'}

iot_data_registries

Gets the iot_data_registries of this ListOfIoTRegistryResponse.

Returns:The iot_data_registries of this ListOfIoTRegistryResponse. Return type:list[IotDataRegistryResponse]

page

Gets the page of this ListOfIoTRegistryResponse.

Returns:The page of this ListOfIoTRegistryResponse. Return type:TokenPage

to_dict()

Returns the model properties as a dict

to_str()

Returns the string representation of the model

sdi.models.list_of_job_ids module

SDI - Semantic Data Interconnect APIs

The Semantic Data Interconnect (SDI) is a collection of APIs that allows the user to unlock the potential of disparate big data by connecting external data. The SDI can infer the schemas of data based on schema-on-read, allow creating a semantic model and perform big data semantic queries. It seamlessly connects to MindSphere’s Integrated Data Lake (IDL), but it can work independently as well. There are two mechanisms that can be used to upload files so that SDI can generate schemas and make data ready for query. The SDI operations are divided into the following groups: Data Registration for SDI This set of APIs is used to organize the incoming data. When configuring a Data Registry, you have the option to update your data based on a replace or append strategy. If you consider a use case where schema may change and incoming data files are completely different every time then replace is a good strategy. The replace strategy will replace the existing schema and data during each data ingest operation whereas the append strategy will update the existing schema and data during each data ingest operation. Custom Data Type for SDI The SDI by default identifies basic data types for each property, such as String, Integer, Float, Date, etc. The user can use this set of APIs to create their own custom data type. The SDI also provides an API under this category to suggest data type based on user-provided sample test values. Data Lake for SDI The SDI can process files uploaded provides endpoints to manage customer’s data lake registration based on tenant id, cloud provider and data lake type. The set of REST endpoint allows to create, update and retrieve base path for their data lake. The IDL customer needs to create an SDI folder that is under the root folder. Any file uploaded in this folder is automatically picked up by SDI to process via IDL notification. Data Ingest for SDI This set of APIs allows user to upload files, start an ingest job for uploaded files, find job status for ingested jobs or retrieve all job statuses. Schema Registry for SDI The SDI provides a way to find the generated schema in this category. Users can find an SDI generated schema for uploaded files based on source name, data tag or schema name. Data Query for SDI allows querying based on the extracted schemas. Important supported APIs are: * Query interface for querying semantically correlated and transformed data. * Stores and executes data queries. * Uses a semantic model to translate model-based query to physical queries. Semantic Model for SDI allows user to create semantic model ontologies based on the extracted one or more schemas. The important functionalities achieved with APIs are: * Contextual correlation of data from different systems. * Infers & Recommends mappings between different schemas. * Import and store Semantic model. # noqa: E501

class ListOfJobIds(ingest_job_status=None, page=None)

Bases: object

Attributes:

attribute_types (dict): The key is attribute name

and the value is attribute type.

attribute_map (dict): The key is attribute name

and the value is json key in definition.

attribute_map = {'ingest_job_status': 'ingestJobStatus', 'page': 'page'}

attribute_types = {'ingest_job_status': 'list[SdiJobStatusResponse]', 'page': 'TokenPage'}

ingest_job_status

Gets the ingest_job_status of this ListOfJobIds.

Returns:The ingest_job_status of this ListOfJobIds. Return type:list[SdiJobStatusResponse]

page

Gets the page of this ListOfJobIds.

Returns:The page of this ListOfJobIds. Return type:TokenPage

to_dict()

Returns the model properties as a dict

to_str()

Returns the string representation of the model

sdi.models.list_of_patterns module

SDI - Semantic Data Interconnect APIs

The Semantic Data Interconnect (SDI) is a collection of APIs that allows the user to unlock the potential of disparate big data by connecting external data. The SDI can infer the schemas of data based on schema-on-read, allow creating a semantic model and perform big data semantic queries. It seamlessly connects to MindSphere’s Integrated Data Lake (IDL), but it can work independently as well. There are two mechanisms that can be used to upload files so that SDI can generate schemas and make data ready for query. The SDI operations are divided into the following groups: Data Registration for SDI This set of APIs is used to organize the incoming data. When configuring a Data Registry, you have the option to update your data based on a replace or append strategy. If you consider a use case where schema may change and incoming data files are completely different every time then replace is a good strategy. The replace strategy will replace the existing schema and data during each data ingest operation whereas the append strategy will update the existing schema and data during each data ingest operation. Custom Data Type for SDI The SDI by default identifies basic data types for each property, such as String, Integer, Float, Date, etc. The user can use this set of APIs to create their own custom data type. The SDI also provides an API under this category to suggest data type based on user-provided sample test values. Data Lake for SDI The SDI can process files uploaded provides endpoints to manage customer’s data lake registration based on tenant id, cloud provider and data lake type. The set of REST endpoint allows to create, update and retrieve base path for their data lake. The IDL customer needs to create an SDI folder that is under the root folder. Any file uploaded in this folder is automatically picked up by SDI to process via IDL notification. Data Ingest for SDI This set of APIs allows user to upload files, start an ingest job for uploaded files, find job status for ingested jobs or retrieve all job statuses. Schema Registry for SDI The SDI provides a way to find the generated schema in this category. Users can find an SDI generated schema for uploaded files based on source name, data tag or schema name. Data Query for SDI allows querying based on the extracted schemas. Important supported APIs are: * Query interface for querying semantically correlated and transformed data. * Stores and executes data queries. * Uses a semantic model to translate model-based query to physical queries. Semantic Model for SDI allows user to create semantic model ontologies based on the extracted one or more schemas. The important functionalities achieved with APIs are: * Contextual correlation of data from different systems. * Infers & Recommends mappings between different schemas. * Import and store Semantic model. # noqa: E501

class ListOfPatterns(suggest_patterns=None)

Bases: object

Attributes:

attribute_types (dict): The key is attribute name

and the value is attribute type.

attribute_map (dict): The key is attribute name

and the value is json key in definition.

attribute_map = {'suggest_patterns': 'suggestPatterns'}

attribute_types = {'suggest_patterns': 'list[Pattern]'}

suggest_patterns

Gets the suggest_patterns of this ListOfPatterns.

Returns:The suggest_patterns of this ListOfPatterns. Return type:list[Pattern]

to_dict()

Returns the model properties as a dict

to_str()

Returns the string representation of the model

sdi.models.list_of_registry_response module

SDI - Semantic Data Interconnect APIs

The Semantic Data Interconnect (SDI) is a collection of APIs that allows the user to unlock the potential of disparate big data by connecting external data. The SDI can infer the schemas of data based on schema-on-read, allow creating a semantic model and perform big data semantic queries. It seamlessly connects to MindSphere’s Integrated Data Lake (IDL), but it can work independently as well. There are two mechanisms that can be used to upload files so that SDI can generate schemas and make data ready for query. The SDI operations are divided into the following groups: Data Registration for SDI This set of APIs is used to organize the incoming data. When configuring a Data Registry, you have the option to update your data based on a replace or append strategy. If you consider a use case where schema may change and incoming data files are completely different every time then replace is a good strategy. The replace strategy will replace the existing schema and data during each data ingest operation whereas the append strategy will update the existing schema and data during each data ingest operation. Custom Data Type for SDI The SDI by default identifies basic data types for each property, such as String, Integer, Float, Date, etc. The user can use this set of APIs to create their own custom data type. The SDI also provides an API under this category to suggest data type based on user-provided sample test values. Data Lake for SDI The SDI can process files uploaded provides endpoints to manage customer’s data lake registration based on tenant id, cloud provider and data lake type. The set of REST endpoint allows to create, update and retrieve base path for their data lake. The IDL customer needs to create an SDI folder that is under the root folder. Any file uploaded in this folder is automatically picked up by SDI to process via IDL notification. Data Ingest for SDI This set of APIs allows user to upload files, start an ingest job for uploaded files, find job status for ingested jobs or retrieve all job statuses. Schema Registry for SDI The SDI provides a way to find the generated schema in this category. Users can find an SDI generated schema for uploaded files based on source name, data tag or schema name. Data Query for SDI allows querying based on the extracted schemas. Important supported APIs are: * Query interface for querying semantically correlated and transformed data. * Stores and executes data queries. * Uses a semantic model to translate model-based query to physical queries. Semantic Model for SDI allows user to create semantic model ontologies based on the extracted one or more schemas. The important functionalities achieved with APIs are: * Contextual correlation of data from different systems. * Infers & Recommends mappings between different schemas. * Import and store Semantic model. # noqa: E501

class ListOfRegistryResponse(data_registries=None, page=None)

Bases: object

Attributes:

attribute_types (dict): The key is attribute name

and the value is attribute type.

attribute_map (dict): The key is attribute name

and the value is json key in definition.

attribute_map = {'data_registries': 'dataRegistries', 'page': 'page'}

attribute_types = {'data_registries': 'list[DataRegistry]', 'page': 'TokenPage'}

data_registries

Gets the data_registries of this ListOfRegistryResponse.

Returns:The data_registries of this ListOfRegistryResponse. Return type:list[DataRegistry]

page

Gets the page of this ListOfRegistryResponse.

Returns:The page of this ListOfRegistryResponse. Return type:TokenPage

to_dict()

Returns the model properties as a dict

to_str()

Returns the string representation of the model

sdi.models.list_of_schema_properties module

SDI - Semantic Data Interconnect APIs

The Semantic Data Interconnect (SDI) is a collection of APIs that allows the user to unlock the potential of disparate big data by connecting external data. The SDI can infer the schemas of data based on schema-on-read, allow creating a semantic model and perform big data semantic queries. It seamlessly connects to MindSphere’s Integrated Data Lake (IDL), but it can work independently as well. There are two mechanisms that can be used to upload files so that SDI can generate schemas and make data ready for query. The SDI operations are divided into the following groups: Data Registration for SDI This set of APIs is used to organize the incoming data. When configuring a Data Registry, you have the option to update your data based on a replace or append strategy. If you consider a use case where schema may change and incoming data files are completely different every time then replace is a good strategy. The replace strategy will replace the existing schema and data during each data ingest operation whereas the append strategy will update the existing schema and data during each data ingest operation. Custom Data Type for SDI The SDI by default identifies basic data types for each property, such as String, Integer, Float, Date, etc. The user can use this set of APIs to create their own custom data type. The SDI also provides an API under this category to suggest data type based on user-provided sample test values. Data Lake for SDI The SDI can process files uploaded provides endpoints to manage customer’s data lake registration based on tenant id, cloud provider and data lake type. The set of REST endpoint allows to create, update and retrieve base path for their data lake. The IDL customer needs to create an SDI folder that is under the root folder. Any file uploaded in this folder is automatically picked up by SDI to process via IDL notification. Data Ingest for SDI This set of APIs allows user to upload files, start an ingest job for uploaded files, find job status for ingested jobs or retrieve all job statuses. Schema Registry for SDI The SDI provides a way to find the generated schema in this category. Users can find an SDI generated schema for uploaded files based on source name, data tag or schema name. Data Query for SDI allows querying based on the extracted schemas. Important supported APIs are: * Query interface for querying semantically correlated and transformed data. * Stores and executes data queries. * Uses a semantic model to translate model-based query to physical queries. Semantic Model for SDI allows user to create semantic model ontologies based on the extracted one or more schemas. The important functionalities achieved with APIs are: * Contextual correlation of data from different systems. * Infers & Recommends mappings between different schemas. * Import and store Semantic model. # noqa: E501

class ListOfSchemaProperties

Bases: object

Attributes:

attribute_types (dict): The key is attribute name

and the value is attribute type.

attribute_map (dict): The key is attribute name

and the value is json key in definition.

attribute_map = {}

attribute_types = {}

to_dict()

Returns the model properties as a dict

to_str()

Returns the string representation of the model

sdi.models.list_of_schema_registry module

SDI - Semantic Data Interconnect APIs

The Semantic Data Interconnect (SDI) is a collection of APIs that allows the user to unlock the potential of disparate big data by connecting external data. The SDI can infer the schemas of data based on schema-on-read, allow creating a semantic model and perform big data semantic queries. It seamlessly connects to MindSphere’s Integrated Data Lake (IDL), but it can work independently as well. There are two mechanisms that can be used to upload files so that SDI can generate schemas and make data ready for query. The SDI operations are divided into the following groups: Data Registration for SDI This set of APIs is used to organize the incoming data. When configuring a Data Registry, you have the option to update your data based on a replace or append strategy. If you consider a use case where schema may change and incoming data files are completely different every time then replace is a good strategy. The replace strategy will replace the existing schema and data during each data ingest operation whereas the append strategy will update the existing schema and data during each data ingest operation. Custom Data Type for SDI The SDI by default identifies basic data types for each property, such as String, Integer, Float, Date, etc. The user can use this set of APIs to create their own custom data type. The SDI also provides an API under this category to suggest data type based on user-provided sample test values. Data Lake for SDI The SDI can process files uploaded provides endpoints to manage customer’s data lake registration based on tenant id, cloud provider and data lake type. The set of REST endpoint allows to create, update and retrieve base path for their data lake. The IDL customer needs to create an SDI folder that is under the root folder. Any file uploaded in this folder is automatically picked up by SDI to process via IDL notification. Data Ingest for SDI This set of APIs allows user to upload files, start an ingest job for uploaded files, find job status for ingested jobs or retrieve all job statuses. Schema Registry for SDI The SDI provides a way to find the generated schema in this category. Users can find an SDI generated schema for uploaded files based on source name, data tag or schema name. Data Query for SDI allows querying based on the extracted schemas. Important supported APIs are: * Query interface for querying semantically correlated and transformed data. * Stores and executes data queries. * Uses a semantic model to translate model-based query to physical queries. Semantic Model for SDI allows user to create semantic model ontologies based on the extracted one or more schemas. The important functionalities achieved with APIs are: * Contextual correlation of data from different systems. * Infers & Recommends mappings between different schemas. * Import and store Semantic model. # noqa: E501

class ListOfSchemaRegistry(schemas=None, page=None)

Bases: object

Attributes:

attribute_types (dict): The key is attribute name

and the value is attribute type.

attribute_map (dict): The key is attribute name

and the value is json key in definition.

attribute_map = {'page': 'page', 'schemas': 'schemas'}

attribute_types = {'page': 'TokenPage', 'schemas': 'list[SDISchemaRegistry]'}

page

Gets the page of this ListOfSchemaRegistry.

Returns:The page of this ListOfSchemaRegistry. Return type:TokenPage

schemas

Gets the schemas of this ListOfSchemaRegistry.

Returns:The schemas of this ListOfSchemaRegistry. Return type:list[SDISchemaRegistry]

to_dict()

Returns the model properties as a dict

to_str()

Returns the string representation of the model

sdi.models.mapping_error_sql_details module

SDI - Semantic Data Interconnect APIs

The Semantic Data Interconnect (SDI) is a collection of APIs that allows the user to unlock the potential of disparate big data by connecting external data. The SDI can infer the schemas of data based on schema-on-read, allow creating a semantic model and perform big data semantic queries. It seamlessly connects to MindSphere’s Integrated Data Lake (IDL), but it can work independently as well. There are two mechanisms that can be used to upload files so that SDI can generate schemas and make data ready for query. The SDI operations are divided into the following groups: Data Registration for SDI This set of APIs is used to organize the incoming data. When configuring a Data Registry, you have the option to update your data based on a replace or append strategy. If you consider a use case where schema may change and incoming data files are completely different every time then replace is a good strategy. The replace strategy will replace the existing schema and data during each data ingest operation whereas the append strategy will update the existing schema and data during each data ingest operation. Custom Data Type for SDI The SDI by default identifies basic data types for each property, such as String, Integer, Float, Date, etc. The user can use this set of APIs to create their own custom data type. The SDI also provides an API under this category to suggest data type based on user-provided sample test values. Data Lake for SDI The SDI can process files uploaded provides endpoints to manage customer’s data lake registration based on tenant id, cloud provider and data lake type. The set of REST endpoint allows to create, update and retrieve base path for their data lake. The IDL customer needs to create an SDI folder that is under the root folder. Any file uploaded in this folder is automatically picked up by SDI to process via IDL notification. Data Ingest for SDI This set of APIs allows user to upload files, start an ingest job for uploaded files, find job status for ingested jobs or retrieve all job statuses. Schema Registry for SDI The SDI provides a way to find the generated schema in this category. Users can find an SDI generated schema for uploaded files based on source name, data tag or schema name. Data Query for SDI allows querying based on the extracted schemas. Important supported APIs are: * Query interface for querying semantically correlated and transformed data. * Stores and executes data queries. * Uses a semantic model to translate model-based query to physical queries. Semantic Model for SDI allows user to create semantic model ontologies based on the extracted one or more schemas. The important functionalities achieved with APIs are: * Contextual correlation of data from different systems. * Infers & Recommends mappings between different schemas. * Import and store Semantic model. # noqa: E501

class MappingErrorSQLDetails(field=None, message=None)

Bases: object

Attributes:

attribute_types (dict): The key is attribute name

and the value is attribute type.

attribute_map (dict): The key is attribute name

and the value is json key in definition.

attribute_map = {'field': 'field', 'message': 'message'}

attribute_types = {'field': 'str', 'message': 'str'}

field

Gets the field of this MappingErrorSQLDetails.

Returns:The field of this MappingErrorSQLDetails. Return type:str

message

Gets the message of this MappingErrorSQLDetails.

Returns:The message of this MappingErrorSQLDetails. Return type:str

to_dict()

Returns the model properties as a dict

to_str()

Returns the string representation of the model

sdi.models.mapping_function module

SDI - Semantic Data Interconnect APIs

The Semantic Data Interconnect (SDI) is a collection of APIs that allows the user to unlock the potential of disparate big data by connecting external data. The SDI can infer the schemas of data based on schema-on-read, allow creating a semantic model and perform big data semantic queries. It seamlessly connects to MindSphere’s Integrated Data Lake (IDL), but it can work independently as well. There are two mechanisms that can be used to upload files so that SDI can generate schemas and make data ready for query. The SDI operations are divided into the following groups: Data Registration for SDI This set of APIs is used to organize the incoming data. When configuring a Data Registry, you have the option to update your data based on a replace or append strategy. If you consider a use case where schema may change and incoming data files are completely different every time then replace is a good strategy. The replace strategy will replace the existing schema and data during each data ingest operation whereas the append strategy will update the existing schema and data during each data ingest operation. Custom Data Type for SDI The SDI by default identifies basic data types for each property, such as String, Integer, Float, Date, etc. The user can use this set of APIs to create their own custom data type. The SDI also provides an API under this category to suggest data type based on user-provided sample test values. Data Lake for SDI The SDI can process files uploaded provides endpoints to manage customer’s data lake registration based on tenant id, cloud provider and data lake type. The set of REST endpoint allows to create, update and retrieve base path for their data lake. The IDL customer needs to create an SDI folder that is under the root folder. Any file uploaded in this folder is automatically picked up by SDI to process via IDL notification. Data Ingest for SDI This set of APIs allows user to upload files, start an ingest job for uploaded files, find job status for ingested jobs or retrieve all job statuses. Schema Registry for SDI The SDI provides a way to find the generated schema in this category. Users can find an SDI generated schema for uploaded files based on source name, data tag or schema name. Data Query for SDI allows querying based on the extracted schemas. Important supported APIs are: * Query interface for querying semantically correlated and transformed data. * Stores and executes data queries. * Uses a semantic model to translate model-based query to physical queries. Semantic Model for SDI allows user to create semantic model ontologies based on the extracted one or more schemas. The important functionalities achieved with APIs are: * Contextual correlation of data from different systems. * Infers & Recommends mappings between different schemas. * Import and store Semantic model. # noqa: E501

class MappingFunction(operator=None)

Bases: object

Attributes:

attribute_types (dict): The key is attribute name

and the value is attribute type.

attribute_map (dict): The key is attribute name

and the value is json key in definition.

attribute_map = {'operator': 'operator'}

attribute_types = {'operator': 'str'}

operator

Gets the operator of this MappingFunction.

Returns:The operator of this MappingFunction. Return type:str

to_dict()

Returns the model properties as a dict

to_str()

Returns the string representation of the model

sdi.models.mdsp_api_error module

SDI - Semantic Data Interconnect APIs

The Semantic Data Interconnect (SDI) is a collection of APIs that allows the user to unlock the potential of disparate big data by connecting external data. The SDI can infer the schemas of data based on schema-on-read, allow creating a semantic model and perform big data semantic queries. It seamlessly connects to MindSphere’s Integrated Data Lake (IDL), but it can work independently as well. There are two mechanisms that can be used to upload files so that SDI can generate schemas and make data ready for query. The SDI operations are divided into the following groups: Data Registration for SDI This set of APIs is used to organize the incoming data. When configuring a Data Registry, you have the option to update your data based on a replace or append strategy. If you consider a use case where schema may change and incoming data files are completely different every time then replace is a good strategy. The replace strategy will replace the existing schema and data during each data ingest operation whereas the append strategy will update the existing schema and data during each data ingest operation. Custom Data Type for SDI The SDI by default identifies basic data types for each property, such as String, Integer, Float, Date, etc. The user can use this set of APIs to create their own custom data type. The SDI also provides an API under this category to suggest data type based on user-provided sample test values. Data Lake for SDI The SDI can process files uploaded provides endpoints to manage customer’s data lake registration based on tenant id, cloud provider and data lake type. The set of REST endpoint allows to create, update and retrieve base path for their data lake. The IDL customer needs to create an SDI folder that is under the root folder. Any file uploaded in this folder is automatically picked up by SDI to process via IDL notification. Data Ingest for SDI This set of APIs allows user to upload files, start an ingest job for uploaded files, find job status for ingested jobs or retrieve all job statuses. Schema Registry for SDI The SDI provides a way to find the generated schema in this category. Users can find an SDI generated schema for uploaded files based on source name, data tag or schema name. Data Query for SDI allows querying based on the extracted schemas. Important supported APIs are: * Query interface for querying semantically correlated and transformed data. * Stores and executes data queries. * Uses a semantic model to translate model-based query to physical queries. Semantic Model for SDI allows user to create semantic model ontologies based on the extracted one or more schemas. The important functionalities achieved with APIs are: * Contextual correlation of data from different systems. * Infers & Recommends mappings between different schemas. * Import and store Semantic model. # noqa: E501

class MdspApiError(code=None, message=None, message_parameters=None)

Bases: object

Attributes:

attribute_types (dict): The key is attribute name

and the value is attribute type.

attribute_map (dict): The key is attribute name

and the value is json key in definition.

attribute_map = {'code': 'code', 'message': 'message', 'message_parameters': 'messageParameters'}

attribute_types = {'code': 'str', 'message': 'str', 'message_parameters': 'list[MdspApiErrorMessageParameters]'}

code

Gets the code of this MdspApiError. Unique error code. Every code is bound to one (parametrized) message.

Returns:The code of this MdspApiError. Return type:str

message

Gets the message of this MdspApiError. Human readable error message in English.

Returns:The message of this MdspApiError. Return type:str

message_parameters

Gets the message_parameters of this MdspApiError. In case an error message is parametrized, the parameter names and values are returned for, e.g., localization purposes. The parametrized error messages are defined at the operation error response descriptions in this API specification. Parameters are denoted by named placeholders ‘{<parameter name>}’ in the message specifications. At runtime, returned message placeholders are substituted by actual parameter values.

Returns:The message_parameters of this MdspApiError. Return type:list[MdspApiErrorMessageParameters]

to_dict()

Returns the model properties as a dict

to_str()

Returns the string representation of the model

sdi.models.mdsp_api_error_message_parameters module

SDI - Semantic Data Interconnect APIs

The Semantic Data Interconnect (SDI) is a collection of APIs that allows the user to unlock the potential of disparate big data by connecting external data. The SDI can infer the schemas of data based on schema-on-read, allow creating a semantic model and perform big data semantic queries. It seamlessly connects to MindSphere’s Integrated Data Lake (IDL), but it can work independently as well. There are two mechanisms that can be used to upload files so that SDI can generate schemas and make data ready for query. The SDI operations are divided into the following groups: Data Registration for SDI This set of APIs is used to organize the incoming data. When configuring a Data Registry, you have the option to update your data based on a replace or append strategy. If you consider a use case where schema may change and incoming data files are completely different every time then replace is a good strategy. The replace strategy will replace the existing schema and data during each data ingest operation whereas the append strategy will update the existing schema and data during each data ingest operation. Custom Data Type for SDI The SDI by default identifies basic data types for each property, such as String, Integer, Float, Date, etc. The user can use this set of APIs to create their own custom data type. The SDI also provides an API under this category to suggest data type based on user-provided sample test values. Data Lake for SDI The SDI can process files uploaded provides endpoints to manage customer’s data lake registration based on tenant id, cloud provider and data lake type. The set of REST endpoint allows to create, update and retrieve base path for their data lake. The IDL customer needs to create an SDI folder that is under the root folder. Any file uploaded in this folder is automatically picked up by SDI to process via IDL notification. Data Ingest for SDI This set of APIs allows user to upload files, start an ingest job for uploaded files, find job status for ingested jobs or retrieve all job statuses. Schema Registry for SDI The SDI provides a way to find the generated schema in this category. Users can find an SDI generated schema for uploaded files based on source name, data tag or schema name. Data Query for SDI allows querying based on the extracted schemas. Important supported APIs are: * Query interface for querying semantically correlated and transformed data. * Stores and executes data queries. * Uses a semantic model to translate model-based query to physical queries. Semantic Model for SDI allows user to create semantic model ontologies based on the extracted one or more schemas. The important functionalities achieved with APIs are: * Contextual correlation of data from different systems. * Infers & Recommends mappings between different schemas. * Import and store Semantic model. # noqa: E501

class MdspApiErrorMessageParameters(name=None, value=None)

Bases: object

Attributes:

attribute_types (dict): The key is attribute name

and the value is attribute type.

attribute_map (dict): The key is attribute name

and the value is json key in definition.

attribute_map = {'name': 'name', 'value': 'value'}

attribute_types = {'name': 'str', 'value': 'str'}

name

Gets the name of this MdspApiErrorMessageParameters. Name of message parameter as specified in parametrized error message.

Returns:The name of this MdspApiErrorMessageParameters. Return type:str

to_dict()

Returns the model properties as a dict

to_str()

Returns the string representation of the model

value

Gets the value of this MdspApiErrorMessageParameters. Value of message parameter as substituted in returned error message.

Returns:The value of this MdspApiErrorMessageParameters. Return type:str

sdi.models.mdsp_error module

SDI - Semantic Data Interconnect APIs

The Semantic Data Interconnect (SDI) is a collection of APIs that allows the user to unlock the potential of disparate big data by connecting external data. The SDI can infer the schemas of data based on schema-on-read, allow creating a semantic model and perform big data semantic queries. It seamlessly connects to MindSphere’s Integrated Data Lake (IDL), but it can work independently as well. There are two mechanisms that can be used to upload files so that SDI can generate schemas and make data ready for query. The SDI operations are divided into the following groups: Data Registration for SDI This set of APIs is used to organize the incoming data. When configuring a Data Registry, you have the option to update your data based on a replace or append strategy. If you consider a use case where schema may change and incoming data files are completely different every time then replace is a good strategy. The replace strategy will replace the existing schema and data during each data ingest operation whereas the append strategy will update the existing schema and data during each data ingest operation. Custom Data Type for SDI The SDI by default identifies basic data types for each property, such as String, Integer, Float, Date, etc. The user can use this set of APIs to create their own custom data type. The SDI also provides an API under this category to suggest data type based on user-provided sample test values. Data Lake for SDI The SDI can process files uploaded provides endpoints to manage customer’s data lake registration based on tenant id, cloud provider and data lake type. The set of REST endpoint allows to create, update and retrieve base path for their data lake. The IDL customer needs to create an SDI folder that is under the root folder. Any file uploaded in this folder is automatically picked up by SDI to process via IDL notification. Data Ingest for SDI This set of APIs allows user to upload files, start an ingest job for uploaded files, find job status for ingested jobs or retrieve all job statuses. Schema Registry for SDI The SDI provides a way to find the generated schema in this category. Users can find an SDI generated schema for uploaded files based on source name, data tag or schema name. Data Query for SDI allows querying based on the extracted schemas. Important supported APIs are: * Query interface for querying semantically correlated and transformed data. * Stores and executes data queries. * Uses a semantic model to translate model-based query to physical queries. Semantic Model for SDI allows user to create semantic model ontologies based on the extracted one or more schemas. The important functionalities achieved with APIs are: * Contextual correlation of data from different systems. * Infers & Recommends mappings between different schemas. * Import and store Semantic model. # noqa: E501

class MdspError(code=None, message=None, message_parameters=None, logref=None)

Bases: object

Attributes:

attribute_types (dict): The key is attribute name

and the value is attribute type.

attribute_map (dict): The key is attribute name

and the value is json key in definition.

attribute_map = {'code': 'code', 'logref': 'logref', 'message': 'message', 'message_parameters': 'messageParameters'}

attribute_types = {'code': 'str', 'logref': 'str', 'message': 'str', 'message_parameters': 'list[MdspApiErrorMessageParameters]'}

code

Gets the code of this MdspError. Unique error code. Every code is bound to one (parametrized) message.

Returns:The code of this MdspError. Return type:str

logref

Gets the logref of this MdspError. Logging correlation ID for debugging purposes.

Returns:The logref of this MdspError. Return type:str

message

Gets the message of this MdspError. Human readable error message in English.

Returns:The message of this MdspError. Return type:str

message_parameters

Gets the message_parameters of this MdspError. In case an error message is parametrized, the parameter names and values are returned for, e.g., localization purposes. The parametrized error messages are defined at the operation error response descriptions in this API specification. Parameters are denoted by named placeholders ‘{<parameter name>}’ in the message specifications. At runtime, returned message placeholders are substituted by actual parameter values.

Returns:The message_parameters of this MdspError. Return type:list[MdspApiErrorMessageParameters]

to_dict()

Returns the model properties as a dict

to_str()

Returns the string representation of the model

sdi.models.mdsp_errors module

SDI - Semantic Data Interconnect APIs

The Semantic Data Interconnect (SDI) is a collection of APIs that allows the user to unlock the potential of disparate big data by connecting external data. The SDI can infer the schemas of data based on schema-on-read, allow creating a semantic model and perform big data semantic queries. It seamlessly connects to MindSphere’s Integrated Data Lake (IDL), but it can work independently as well. There are two mechanisms that can be used to upload files so that SDI can generate schemas and make data ready for query. The SDI operations are divided into the following groups: Data Registration for SDI This set of APIs is used to organize the incoming data. When configuring a Data Registry, you have the option to update your data based on a replace or append strategy. If you consider a use case where schema may change and incoming data files are completely different every time then replace is a good strategy. The replace strategy will replace the existing schema and data during each data ingest operation whereas the append strategy will update the existing schema and data during each data ingest operation. Custom Data Type for SDI The SDI by default identifies basic data types for each property, such as String, Integer, Float, Date, etc. The user can use this set of APIs to create their own custom data type. The SDI also provides an API under this category to suggest data type based on user-provided sample test values. Data Lake for SDI The SDI can process files uploaded provides endpoints to manage customer’s data lake registration based on tenant id, cloud provider and data lake type. The set of REST endpoint allows to create, update and retrieve base path for their data lake. The IDL customer needs to create an SDI folder that is under the root folder. Any file uploaded in this folder is automatically picked up by SDI to process via IDL notification. Data Ingest for SDI This set of APIs allows user to upload files, start an ingest job for uploaded files, find job status for ingested jobs or retrieve all job statuses. Schema Registry for SDI The SDI provides a way to find the generated schema in this category. Users can find an SDI generated schema for uploaded files based on source name, data tag or schema name. Data Query for SDI allows querying based on the extracted schemas. Important supported APIs are: * Query interface for querying semantically correlated and transformed data. * Stores and executes data queries. * Uses a semantic model to translate model-based query to physical queries. Semantic Model for SDI allows user to create semantic model ontologies based on the extracted one or more schemas. The important functionalities achieved with APIs are: * Contextual correlation of data from different systems. * Infers & Recommends mappings between different schemas. * Import and store Semantic model. # noqa: E501

class MdspErrors(errors=None)

Bases: object

Attributes:

attribute_types (dict): The key is attribute name

and the value is attribute type.

attribute_map (dict): The key is attribute name

and the value is json key in definition.

attribute_map = {'errors': 'errors'}

attribute_types = {'errors': 'list[MdspError]'}

errors

Gets the errors of this MdspErrors.

Returns:The errors of this MdspErrors. Return type:list[MdspError]

to_dict()

Returns the model properties as a dict

to_str()

Returns the string representation of the model

sdi.models.message_parameter module

SDI - Semantic Data Interconnect APIs

The Semantic Data Interconnect (SDI) is a collection of APIs that allows the user to unlock the potential of disparate big data by connecting external data. The SDI can infer the schemas of data based on schema-on-read, allow creating a semantic model and perform big data semantic queries. It seamlessly connects to MindSphere’s Integrated Data Lake (IDL), but it can work independently as well. There are two mechanisms that can be used to upload files so that SDI can generate schemas and make data ready for query. The SDI operations are divided into the following groups: Data Registration for SDI This set of APIs is used to organize the incoming data. When configuring a Data Registry, you have the option to update your data based on a replace or append strategy. If you consider a use case where schema may change and incoming data files are completely different every time then replace is a good strategy. The replace strategy will replace the existing schema and data during each data ingest operation whereas the append strategy will update the existing schema and data during each data ingest operation. Custom Data Type for SDI The SDI by default identifies basic data types for each property, such as String, Integer, Float, Date, etc. The user can use this set of APIs to create their own custom data type. The SDI also provides an API under this category to suggest data type based on user-provided sample test values. Data Lake for SDI The SDI can process files uploaded provides endpoints to manage customer’s data lake registration based on tenant id, cloud provider and data lake type. The set of REST endpoint allows to create, update and retrieve base path for their data lake. The IDL customer needs to create an SDI folder that is under the root folder. Any file uploaded in this folder is automatically picked up by SDI to process via IDL notification. Data Ingest for SDI This set of APIs allows user to upload files, start an ingest job for uploaded files, find job status for ingested jobs or retrieve all job statuses. Schema Registry for SDI The SDI provides a way to find the generated schema in this category. Users can find an SDI generated schema for uploaded files based on source name, data tag or schema name. Data Query for SDI allows querying based on the extracted schemas. Important supported APIs are: * Query interface for querying semantically correlated and transformed data. * Stores and executes data queries. * Uses a semantic model to translate model-based query to physical queries. Semantic Model for SDI allows user to create semantic model ontologies based on the extracted one or more schemas. The important functionalities achieved with APIs are: * Contextual correlation of data from different systems. * Infers & Recommends mappings between different schemas. * Import and store Semantic model. # noqa: E501

class MessageParameter(name=None, value=None)

Bases: object

Attributes:

attribute_types (dict): The key is attribute name

and the value is attribute type.

attribute_map (dict): The key is attribute name

and the value is json key in definition.

attribute_map = {'name': 'name', 'value': 'value'}

attribute_types = {'name': 'str', 'value': 'object'}

name

Gets the name of this MessageParameter.

Returns:The name of this MessageParameter. Return type:str

to_dict()

Returns the model properties as a dict

to_str()

Returns the string representation of the model

value

Gets the value of this MessageParameter.

Returns:The value of this MessageParameter. Return type:object

sdi.models.native_query_get_response module

SDI - Semantic Data Interconnect APIs

The Semantic Data Interconnect (SDI) is a collection of APIs that allows the user to unlock the potential of disparate big data by connecting external data. The SDI can infer the schemas of data based on schema-on-read, allow creating a semantic model and perform big data semantic queries. It seamlessly connects to MindSphere’s Integrated Data Lake (IDL), but it can work independently as well. There are two mechanisms that can be used to upload files so that SDI can generate schemas and make data ready for query. The SDI operations are divided into the following groups: Data Registration for SDI This set of APIs is used to organize the incoming data. When configuring a Data Registry, you have the option to update your data based on a replace or append strategy. If you consider a use case where schema may change and incoming data files are completely different every time then replace is a good strategy. The replace strategy will replace the existing schema and data during each data ingest operation whereas the append strategy will update the existing schema and data during each data ingest operation. Custom Data Type for SDI The SDI by default identifies basic data types for each property, such as String, Integer, Float, Date, etc. The user can use this set of APIs to create their own custom data type. The SDI also provides an API under this category to suggest data type based on user-provided sample test values. Data Lake for SDI The SDI can process files uploaded provides endpoints to manage customer’s data lake registration based on tenant id, cloud provider and data lake type. The set of REST endpoint allows to create, update and retrieve base path for their data lake. The IDL customer needs to create an SDI folder that is under the root folder. Any file uploaded in this folder is automatically picked up by SDI to process via IDL notification. Data Ingest for SDI This set of APIs allows user to upload files, start an ingest job for uploaded files, find job status for ingested jobs or retrieve all job statuses. Schema Registry for SDI The SDI provides a way to find the generated schema in this category. Users can find an SDI generated schema for uploaded files based on source name, data tag or schema name. Data Query for SDI allows querying based on the extracted schemas. Important supported APIs are: * Query interface for querying semantically correlated and transformed data. * Stores and executes data queries. * Uses a semantic model to translate model-based query to physical queries. Semantic Model for SDI allows user to create semantic model ontologies based on the extracted one or more schemas. The important functionalities achieved with APIs are: * Contextual correlation of data from different systems. * Infers & Recommends mappings between different schemas. * Import and store Semantic model. # noqa: E501

class NativeQueryGetResponse(created_date=None, description=None, executable=None, id=None, is_business_query=None, is_dynamic=None, name=None, ontology_id=None, pending_actions=None, sql_statement=None, updated_date=None, last_ten_execution_job_ids=None)

Bases: object

Attributes:

attribute_types (dict): The key is attribute name

and the value is attribute type.

attribute_map (dict): The key is attribute name

and the value is json key in definition.

attribute_map = {'created_date': 'createdDate', 'description': 'description', 'executable': 'executable', 'id': 'id', 'is_business_query': 'isBusinessQuery', 'is_dynamic': 'isDynamic', 'last_ten_execution_job_ids': 'lastTenExecutionJobIds', 'name': 'name', 'ontology_id': 'ontologyId', 'pending_actions': 'pendingActions', 'sql_statement': 'sqlStatement', 'updated_date': 'updatedDate'}

attribute_types = {'created_date': 'str', 'description': 'str', 'executable': 'bool', 'id': 'str', 'is_business_query': 'bool', 'is_dynamic': 'bool', 'last_ten_execution_job_ids': 'list[str]', 'name': 'str', 'ontology_id': 'str', 'pending_actions': 'list[MappingErrorSQLDetails]', 'sql_statement': 'str', 'updated_date': 'str'}

created_date

Gets the created_date of this NativeQueryGetResponse.

Returns:The created_date of this NativeQueryGetResponse. Return type:str

description

Gets the description of this NativeQueryGetResponse.

Returns:The description of this NativeQueryGetResponse. Return type:str

executable

Gets the executable of this NativeQueryGetResponse.

Returns:The executable of this NativeQueryGetResponse. Return type:bool

id

Gets the id of this NativeQueryGetResponse.

Returns:The id of this NativeQueryGetResponse. Return type:str

is_business_query

Gets the is_business_query of this NativeQueryGetResponse.

Returns:The is_business_query of this NativeQueryGetResponse. Return type:bool

is_dynamic

Gets the is_dynamic of this NativeQueryGetResponse.

Returns:The is_dynamic of this NativeQueryGetResponse. Return type:bool

last_ten_execution_job_ids

Gets the last_ten_execution_job_ids of this NativeQueryGetResponse.

Returns:The last_ten_execution_job_ids of this NativeQueryGetResponse. Return type:list[str]

name

Gets the name of this NativeQueryGetResponse.

Returns:The name of this NativeQueryGetResponse. Return type:str

ontology_id

Gets the ontology_id of this NativeQueryGetResponse.

Returns:The ontology_id of this NativeQueryGetResponse. Return type:str

pending_actions

Gets the pending_actions of this NativeQueryGetResponse.

Returns:The pending_actions of this NativeQueryGetResponse. Return type:list[MappingErrorSQLDetails]

sql_statement

Gets the sql_statement of this NativeQueryGetResponse.

Returns:The sql_statement of this NativeQueryGetResponse. Return type:str

to_dict()

Returns the model properties as a dict

to_str()

Returns the string representation of the model

updated_date

Gets the updated_date of this NativeQueryGetResponse.

Returns:The updated_date of this NativeQueryGetResponse. Return type:str

sdi.models.null_request module

SDI - Semantic Data Interconnect APIs

The Semantic Data Interconnect (SDI) is a collection of APIs that allows the user to unlock the potential of disparate big data by connecting external data. The SDI can infer the schemas of data based on schema-on-read, allow creating a semantic model and perform big data semantic queries. It seamlessly connects to MindSphere’s Integrated Data Lake (IDL), but it can work independently as well. There are two mechanisms that can be used to upload files so that SDI can generate schemas and make data ready for query. The SDI operations are divided into the following groups: Data Registration for SDI This set of APIs is used to organize the incoming data. When configuring a Data Registry, you have the option to update your data based on a replace or append strategy. If you consider a use case where schema may change and incoming data files are completely different every time then replace is a good strategy. The replace strategy will replace the existing schema and data during each data ingest operation whereas the append strategy will update the existing schema and data during each data ingest operation. Custom Data Type for SDI The SDI by default identifies basic data types for each property, such as String, Integer, Float, Date, etc. The user can use this set of APIs to create their own custom data type. The SDI also provides an API under this category to suggest data type based on user-provided sample test values. Data Lake for SDI The SDI can process files uploaded provides endpoints to manage customer’s data lake registration based on tenant id, cloud provider and data lake type. The set of REST endpoint allows to create, update and retrieve base path for their data lake. The IDL customer needs to create an SDI folder that is under the root folder. Any file uploaded in this folder is automatically picked up by SDI to process via IDL notification. Data Ingest for SDI This set of APIs allows user to upload files, start an ingest job for uploaded files, find job status for ingested jobs or retrieve all job statuses. Schema Registry for SDI The SDI provides a way to find the generated schema in this category. Users can find an SDI generated schema for uploaded files based on source name, data tag or schema name. Data Query for SDI allows querying based on the extracted schemas. Important supported APIs are: * Query interface for querying semantically correlated and transformed data. * Stores and executes data queries. * Uses a semantic model to translate model-based query to physical queries. Semantic Model for SDI allows user to create semantic model ontologies based on the extracted one or more schemas. The important functionalities achieved with APIs are: * Contextual correlation of data from different systems. * Infers & Recommends mappings between different schemas. * Import and store Semantic model. # noqa: E501

class NullRequest(infer_schema_search_request=None)

Bases: object

Attributes:

attribute_types (dict): The key is attribute name

and the value is attribute type.

attribute_map (dict): The key is attribute name

and the value is json key in definition.

attribute_map = {'infer_schema_search_request': 'inferSchemaSearchRequest'}

attribute_types = {'infer_schema_search_request': 'InferSchemaSearchRequest'}

infer_schema_search_request

Gets the infer_schema_search_request of this NullRequest.

Returns:The infer_schema_search_request of this NullRequest. Return type:InferSchemaSearchRequest

to_dict()

Returns the model properties as a dict

to_str()

Returns the string representation of the model

sdi.models.ontology_create_request module

SDI - Semantic Data Interconnect APIs

The Semantic Data Interconnect (SDI) is a collection of APIs that allows the user to unlock the potential of disparate big data by connecting external data. The SDI can infer the schemas of data based on schema-on-read, allow creating a semantic model and perform big data semantic queries. It seamlessly connects to MindSphere’s Integrated Data Lake (IDL), but it can work independently as well. There are two mechanisms that can be used to upload files so that SDI can generate schemas and make data ready for query. The SDI operations are divided into the following groups: Data Registration for SDI This set of APIs is used to organize the incoming data. When configuring a Data Registry, you have the option to update your data based on a replace or append strategy. If you consider a use case where schema may change and incoming data files are completely different every time then replace is a good strategy. The replace strategy will replace the existing schema and data during each data ingest operation whereas the append strategy will update the existing schema and data during each data ingest operation. Custom Data Type for SDI The SDI by default identifies basic data types for each property, such as String, Integer, Float, Date, etc. The user can use this set of APIs to create their own custom data type. The SDI also provides an API under this category to suggest data type based on user-provided sample test values. Data Lake for SDI The SDI can process files uploaded provides endpoints to manage customer’s data lake registration based on tenant id, cloud provider and data lake type. The set of REST endpoint allows to create, update and retrieve base path for their data lake. The IDL customer needs to create an SDI folder that is under the root folder. Any file uploaded in this folder is automatically picked up by SDI to process via IDL notification. Data Ingest for SDI This set of APIs allows user to upload files, start an ingest job for uploaded files, find job status for ingested jobs or retrieve all job statuses. Schema Registry for SDI The SDI provides a way to find the generated schema in this category. Users can find an SDI generated schema for uploaded files based on source name, data tag or schema name. Data Query for SDI allows querying based on the extracted schemas. Important supported APIs are: * Query interface for querying semantically correlated and transformed data. * Stores and executes data queries. * Uses a semantic model to translate model-based query to physical queries. Semantic Model for SDI allows user to create semantic model ontologies based on the extracted one or more schemas. The important functionalities achieved with APIs are: * Contextual correlation of data from different systems. * Infers & Recommends mappings between different schemas. * Import and store Semantic model. # noqa: E501

class OntologyCreateRequest(ontology_description=None, id=None, ontology_name=None, key_mapping_type=None, file=None)

Bases: object

Attributes:

attribute_types (dict): The key is attribute name

and the value is attribute type.

attribute_map (dict): The key is attribute name

and the value is json key in definition.

attribute_map = {'file': 'file', 'id': 'id', 'key_mapping_type': 'keyMappingType', 'ontology_description': 'ontologyDescription', 'ontology_name': 'ontologyName'}

attribute_types = {'file': 'file', 'id': 'str', 'key_mapping_type': 'str', 'ontology_description': 'str', 'ontology_name': 'str'}

file

Gets the file of this OntologyMetadata.

Returns:The file of this OntologyMetadata. Return type:str

id

Gets the id of this OntologyMetadata. Ontology id.

Returns:The id of this OntologyMetadata. Return type:str

key_mapping_type

Gets the key_mapping_type of this OntologyMetadata. Ontology keyMappingType.

Returns:The key_mapping_type of this OntologyMetadata. Return type:str

ontology_description

Gets the ontology_description of this OntologyMetadata. Ontology description.

Returns:The ontology_description of this OntologyMetadata. Return type:str

ontology_name

Gets the ontology_name of this OntologyMetadata. Ontology name.

Returns:The ontology_name of this OntologyMetadata. Return type:str

to_dict()

Returns the model properties as a dict

to_str()

Returns the string representation of the model

sdi.models.ontology_job module

SDI - Semantic Data Interconnect APIs

The Semantic Data Interconnect (SDI) is a collection of APIs that allows the user to unlock the potential of disparate big data by connecting external data. The SDI can infer the schemas of data based on schema-on-read, allow creating a semantic model and perform big data semantic queries. It seamlessly connects to MindSphere’s Integrated Data Lake (IDL), but it can work independently as well. There are two mechanisms that can be used to upload files so that SDI can generate schemas and make data ready for query. The SDI operations are divided into the following groups: Data Registration for SDI This set of APIs is used to organize the incoming data. When configuring a Data Registry, you have the option to update your data based on a replace or append strategy. If you consider a use case where schema may change and incoming data files are completely different every time then replace is a good strategy. The replace strategy will replace the existing schema and data during each data ingest operation whereas the append strategy will update the existing schema and data during each data ingest operation. Custom Data Type for SDI The SDI by default identifies basic data types for each property, such as String, Integer, Float, Date, etc. The user can use this set of APIs to create their own custom data type. The SDI also provides an API under this category to suggest data type based on user-provided sample test values. Data Lake for SDI The SDI can process files uploaded provides endpoints to manage customer’s data lake registration based on tenant id, cloud provider and data lake type. The set of REST endpoint allows to create, update and retrieve base path for their data lake. The IDL customer needs to create an SDI folder that is under the root folder. Any file uploaded in this folder is automatically picked up by SDI to process via IDL notification. Data Ingest for SDI This set of APIs allows user to upload files, start an ingest job for uploaded files, find job status for ingested jobs or retrieve all job statuses. Schema Registry for SDI The SDI provides a way to find the generated schema in this category. Users can find an SDI generated schema for uploaded files based on source name, data tag or schema name. Data Query for SDI allows querying based on the extracted schemas. Important supported APIs are: * Query interface for querying semantically correlated and transformed data. * Stores and executes data queries. * Uses a semantic model to translate model-based query to physical queries. Semantic Model for SDI allows user to create semantic model ontologies based on the extracted one or more schemas. The important functionalities achieved with APIs are: * Contextual correlation of data from different systems. * Infers & Recommends mappings between different schemas. * Import and store Semantic model. # noqa: E501

class OntologyJob(id=None, status=None, message=None, created_date=None, updated_date=None)

Bases: object

Attributes:

attribute_types (dict): The key is attribute name

and the value is attribute type.

attribute_map (dict): The key is attribute name

and the value is json key in definition.

attribute_map = {'created_date': 'createdDate', 'id': 'id', 'message': 'message', 'status': 'status', 'updated_date': 'updatedDate'}

attribute_types = {'created_date': 'str', 'id': 'str', 'message': 'str', 'status': 'str', 'updated_date': 'str'}

created_date

Gets the created_date of this OntologyJob. Start time of Ontology job created in UTC date format.

Returns:The created_date of this OntologyJob. Return type:str

id

Gets the id of this OntologyJob. Unique Ontology job ID.

Returns:The id of this OntologyJob. Return type:str

message

Gets the message of this OntologyJob. Contains an message in case the job created. Possible messages: - The Resuest for Create Ontology. - The Resuest for Create Ontology using owl file upload. - The Resuest for Update Ontology.

Returns:The message of this OntologyJob. Return type:str

status

Gets the status of this OntologyJob. Status of ontology creation/updation job. - SUBMITTED: job has been created but creation/updation of ontology not yet started. - IN_PROGRESS: Ontology creation or updation started. - FAILED: Ontology creation or updation has failed. No data is available to be retrieved. - SUCCESS: Ontology creation or updation has been successfully finished.

Returns:The status of this OntologyJob. Return type:str

to_dict()

Returns the model properties as a dict

to_str()

Returns the string representation of the model

updated_date

Gets the updated_date of this OntologyJob. Job last modified time in UTC date format. The backend updates this time whenever the job status changes.

Returns:The updated_date of this OntologyJob. Return type:str

sdi.models.ontology_metadata module

SDI - Semantic Data Interconnect APIs

The Semantic Data Interconnect (SDI) is a collection of APIs that allows the user to unlock the potential of disparate big data by connecting external data. The SDI can infer the schemas of data based on schema-on-read, allow creating a semantic model and perform big data semantic queries. It seamlessly connects to MindSphere’s Integrated Data Lake (IDL), but it can work independently as well. There are two mechanisms that can be used to upload files so that SDI can generate schemas and make data ready for query. The SDI operations are divided into the following groups: Data Registration for SDI This set of APIs is used to organize the incoming data. When configuring a Data Registry, you have the option to update your data based on a replace or append strategy. If you consider a use case where schema may change and incoming data files are completely different every time then replace is a good strategy. The replace strategy will replace the existing schema and data during each data ingest operation whereas the append strategy will update the existing schema and data during each data ingest operation. Custom Data Type for SDI The SDI by default identifies basic data types for each property, such as String, Integer, Float, Date, etc. The user can use this set of APIs to create their own custom data type. The SDI also provides an API under this category to suggest data type based on user-provided sample test values. Data Lake for SDI The SDI can process files uploaded provides endpoints to manage customer’s data lake registration based on tenant id, cloud provider and data lake type. The set of REST endpoint allows to create, update and retrieve base path for their data lake. The IDL customer needs to create an SDI folder that is under the root folder. Any file uploaded in this folder is automatically picked up by SDI to process via IDL notification. Data Ingest for SDI This set of APIs allows user to upload files, start an ingest job for uploaded files, find job status for ingested jobs or retrieve all job statuses. Schema Registry for SDI The SDI provides a way to find the generated schema in this category. Users can find an SDI generated schema for uploaded files based on source name, data tag or schema name. Data Query for SDI allows querying based on the extracted schemas. Important supported APIs are: * Query interface for querying semantically correlated and transformed data. * Stores and executes data queries. * Uses a semantic model to translate model-based query to physical queries. Semantic Model for SDI allows user to create semantic model ontologies based on the extracted one or more schemas. The important functionalities achieved with APIs are: * Contextual correlation of data from different systems. * Infers & Recommends mappings between different schemas. * Import and store Semantic model. # noqa: E501

class OntologyMetadata(created_date=None, ontology_description=None, id=None, ontology_name=None, key_mapping_type=None, updated_date=None, file=None)

Bases: object

Attributes:

attribute_types (dict): The key is attribute name

and the value is attribute type.

attribute_map (dict): The key is attribute name

and the value is json key in definition.

attribute_map = {'created_date': 'createdDate', 'file': 'file', 'id': 'id', 'key_mapping_type': 'keyMappingType', 'ontology_description': 'ontologyDescription', 'ontology_name': 'ontologyName', 'updated_date': 'updatedDate'}

attribute_types = {'created_date': 'str', 'file': 'file', 'id': 'str', 'key_mapping_type': 'str', 'ontology_description': 'str', 'ontology_name': 'str', 'updated_date': 'str'}

created_date

Gets the created_date of this OntologyMetadata.

Returns:The created_date of this OntologyMetadata. Return type:str

file

Gets the file of this OntologyMetadata.

Returns:The file of this OntologyMetadata. Return type:str

id

Gets the id of this OntologyMetadata. Ontology id.

Returns:The id of this OntologyMetadata. Return type:str

key_mapping_type

Gets the key_mapping_type of this OntologyMetadata. Ontology keyMappingType.

Returns:The key_mapping_type of this OntologyMetadata. Return type:str

ontology_description

Gets the ontology_description of this OntologyMetadata. Ontology description.

Returns:The ontology_description of this OntologyMetadata. Return type:str

ontology_name

Gets the ontology_name of this OntologyMetadata. Ontology name.

Returns:The ontology_name of this OntologyMetadata. Return type:str

to_dict()

Returns the model properties as a dict

to_str()

Returns the string representation of the model

updated_date

Gets the updated_date of this OntologyMetadata.

Returns:The updated_date of this OntologyMetadata. Return type:str

sdi.models.ontology_response_data module

SDI - Semantic Data Interconnect APIs

The Semantic Data Interconnect (SDI) is a collection of APIs that allows the user to unlock the potential of disparate big data by connecting external data. The SDI can infer the schemas of data based on schema-on-read, allow creating a semantic model and perform big data semantic queries. It seamlessly connects to MindSphere’s Integrated Data Lake (IDL), but it can work independently as well. There are two mechanisms that can be used to upload files so that SDI can generate schemas and make data ready for query. The SDI operations are divided into the following groups: Data Registration for SDI This set of APIs is used to organize the incoming data. When configuring a Data Registry, you have the option to update your data based on a replace or append strategy. If you consider a use case where schema may change and incoming data files are completely different every time then replace is a good strategy. The replace strategy will replace the existing schema and data during each data ingest operation whereas the append strategy will update the existing schema and data during each data ingest operation. Custom Data Type for SDI The SDI by default identifies basic data types for each property, such as String, Integer, Float, Date, etc. The user can use this set of APIs to create their own custom data type. The SDI also provides an API under this category to suggest data type based on user-provided sample test values. Data Lake for SDI The SDI can process files uploaded provides endpoints to manage customer’s data lake registration based on tenant id, cloud provider and data lake type. The set of REST endpoint allows to create, update and retrieve base path for their data lake. The IDL customer needs to create an SDI folder that is under the root folder. Any file uploaded in this folder is automatically picked up by SDI to process via IDL notification. Data Ingest for SDI This set of APIs allows user to upload files, start an ingest job for uploaded files, find job status for ingested jobs or retrieve all job statuses. Schema Registry for SDI The SDI provides a way to find the generated schema in this category. Users can find an SDI generated schema for uploaded files based on source name, data tag or schema name. Data Query for SDI allows querying based on the extracted schemas. Important supported APIs are: * Query interface for querying semantically correlated and transformed data. * Stores and executes data queries. * Uses a semantic model to translate model-based query to physical queries. Semantic Model for SDI allows user to create semantic model ontologies based on the extracted one or more schemas. The important functionalities achieved with APIs are: * Contextual correlation of data from different systems. * Infers & Recommends mappings between different schemas. * Import and store Semantic model. # noqa: E501

class OntologyResponseData(created_date=None, ontology_description=None, id=None, ontology_name=None, key_mapping_type=None, updated_date=None, class_properties=None, classes=None, mappings=None, property_relations=None, schema_properties=None, schemas=None)

Bases: object

Attributes:

attribute_types (dict): The key is attribute name

and the value is attribute type.

attribute_map (dict): The key is attribute name

and the value is json key in definition.

attribute_map = {'class_properties': 'classProperties', 'classes': 'classes', 'created_date': 'createdDate', 'id': 'id', 'key_mapping_type': 'keyMappingType', 'mappings': 'mappings', 'ontology_description': 'ontologyDescription', 'ontology_name': 'ontologyName', 'property_relations': 'propertyRelations', 'schema_properties': 'schemaProperties', 'schemas': 'schemas', 'updated_date': 'updatedDate'}

attribute_types = {'class_properties': 'list[InputClassProperty]', 'classes': 'list[InputClass]', 'created_date': 'str', 'id': 'str', 'key_mapping_type': 'str', 'mappings': 'list[InputMapping]', 'ontology_description': 'str', 'ontology_name': 'str', 'property_relations': 'list[InputPropertyRelation]', 'schema_properties': 'list[InputSchemaProperty]', 'schemas': 'list[InputSchema]', 'updated_date': 'str'}

class_properties

Gets the class_properties of this OntologyResponseData.

Returns:The class_properties of this OntologyResponseData. Return type:list[InputClassProperty]

classes

Gets the classes of this OntologyResponseData.

Returns:The classes of this OntologyResponseData. Return type:list[InputClass]

created_date

Gets the created_date of this OntologyResponseData.

Returns:The created_date of this OntologyResponseData. Return type:str

id

Gets the id of this OntologyResponseData. Ontology id.

Returns:The id of this OntologyResponseData. Return type:str

key_mapping_type

Gets the key_mapping_type of this OntologyResponseData. Ontology keyMappingType.

Returns:The key_mapping_type of this OntologyResponseData. Return type:str

mappings

Gets the mappings of this OntologyResponseData.

Returns:The mappings of this OntologyResponseData. Return type:list[InputMapping]

ontology_description

Gets the ontology_description of this OntologyResponseData. Ontology description.

Returns:The ontology_description of this OntologyResponseData. Return type:str

ontology_name

Gets the ontology_name of this OntologyResponseData. Ontology name.

Returns:The ontology_name of this OntologyResponseData. Return type:str

property_relations

Gets the property_relations of this OntologyResponseData.

Returns:The property_relations of this OntologyResponseData. Return type:list[InputPropertyRelation]

schema_properties

Gets the schema_properties of this OntologyResponseData.

Returns:The schema_properties of this OntologyResponseData. Return type:list[InputSchemaProperty]

schemas

Gets the schemas of this OntologyResponseData.

Returns:The schemas of this OntologyResponseData. Return type:list[InputSchema]

to_dict()

Returns the model properties as a dict

to_str()

Returns the string representation of the model

updated_date

Gets the updated_date of this OntologyResponseData.

Returns:The updated_date of this OntologyResponseData. Return type:str

sdi.models.page_token module

SDI - Semantic Data Interconnect APIs

The Semantic Data Interconnect (SDI) is a collection of APIs that allows the user to unlock the potential of disparate big data by connecting external data. The SDI can infer the schemas of data based on schema-on-read, allow creating a semantic model and perform big data semantic queries. It seamlessly connects to MindSphere’s Integrated Data Lake (IDL), but it can work independently as well. There are two mechanisms that can be used to upload files so that SDI can generate schemas and make data ready for query. The SDI operations are divided into the following groups: Data Registration for SDI This set of APIs is used to organize the incoming data. When configuring a Data Registry, you have the option to update your data based on a replace or append strategy. If you consider a use case where schema may change and incoming data files are completely different every time then replace is a good strategy. The replace strategy will replace the existing schema and data during each data ingest operation whereas the append strategy will update the existing schema and data during each data ingest operation. Custom Data Type for SDI The SDI by default identifies basic data types for each property, such as String, Integer, Float, Date, etc. The user can use this set of APIs to create their own custom data type. The SDI also provides an API under this category to suggest data type based on user-provided sample test values. Data Lake for SDI The SDI can process files uploaded provides endpoints to manage customer’s data lake registration based on tenant id, cloud provider and data lake type. The set of REST endpoint allows to create, update and retrieve base path for their data lake. The IDL customer needs to create an SDI folder that is under the root folder. Any file uploaded in this folder is automatically picked up by SDI to process via IDL notification. Data Ingest for SDI This set of APIs allows user to upload files, start an ingest job for uploaded files, find job status for ingested jobs or retrieve all job statuses. Schema Registry for SDI The SDI provides a way to find the generated schema in this category. Users can find an SDI generated schema for uploaded files based on source name, data tag or schema name. Data Query for SDI allows querying based on the extracted schemas. Important supported APIs are: * Query interface for querying semantically correlated and transformed data. * Stores and executes data queries. * Uses a semantic model to translate model-based query to physical queries. Semantic Model for SDI allows user to create semantic model ontologies based on the extracted one or more schemas. The important functionalities achieved with APIs are: * Contextual correlation of data from different systems. * Infers & Recommends mappings between different schemas. * Import and store Semantic model. # noqa: E501

class PageToken(page_token=None, alias_value=None)

Bases: object

Attributes:

attribute_types (dict): The key is attribute name

and the value is attribute type.

attribute_map (dict): The key is attribute name

and the value is json key in definition.

attribute_map = {'page_token': 'page_value'}

attribute_types = {'page_token': 'str'}

page_token

Gets the page_token of this PageToken.

Returns:The page_token of this PageToken. Return type:str

to_dict()

Returns the model properties as a dict

to_str()

Returns the string representation of the model

sdi.models.parameters module

SDI - Semantic Data Interconnect APIs

The Semantic Data Interconnect (SDI) is a collection of APIs that allows the user to unlock the potential of disparate big data by connecting external data. The SDI can infer the schemas of data based on schema-on-read, allow creating a semantic model and perform big data semantic queries. It seamlessly connects to MindSphere’s Integrated Data Lake (IDL), but it can work independently as well. There are two mechanisms that can be used to upload files so that SDI can generate schemas and make data ready for query. The SDI operations are divided into the following groups: Data Registration for SDI This set of APIs is used to organize the incoming data. When configuring a Data Registry, you have the option to update your data based on a replace or append strategy. If you consider a use case where schema may change and incoming data files are completely different every time then replace is a good strategy. The replace strategy will replace the existing schema and data during each data ingest operation whereas the append strategy will update the existing schema and data during each data ingest operation. Custom Data Type for SDI The SDI by default identifies basic data types for each property, such as String, Integer, Float, Date, etc. The user can use this set of APIs to create their own custom data type. The SDI also provides an API under this category to suggest data type based on user-provided sample test values. Data Lake for SDI The SDI can process files uploaded provides endpoints to manage customer’s data lake registration based on tenant id, cloud provider and data lake type. The set of REST endpoint allows to create, update and retrieve base path for their data lake. The IDL customer needs to create an SDI folder that is under the root folder. Any file uploaded in this folder is automatically picked up by SDI to process via IDL notification. Data Ingest for SDI This set of APIs allows user to upload files, start an ingest job for uploaded files, find job status for ingested jobs or retrieve all job statuses. Schema Registry for SDI The SDI provides a way to find the generated schema in this category. Users can find an SDI generated schema for uploaded files based on source name, data tag or schema name. Data Query for SDI allows querying based on the extracted schemas. Important supported APIs are: * Query interface for querying semantically correlated and transformed data. * Stores and executes data queries. * Uses a semantic model to translate model-based query to physical queries. Semantic Model for SDI allows user to create semantic model ontologies based on the extracted one or more schemas. The important functionalities achieved with APIs are: * Contextual correlation of data from different systems. * Infers & Recommends mappings between different schemas. * Import and store Semantic model. # noqa: E501

class Parameters(param_name=None, param_value=None)

Bases: object

Attributes:

attribute_types (dict): The key is attribute name

and the value is attribute type.

attribute_map (dict): The key is attribute name

and the value is json key in definition.

attribute_map = {'param_name': 'paramName', 'param_value': 'paramValue'}

attribute_types = {'param_name': 'str', 'param_value': 'str'}

param_name

Gets the param_name of this Parameters.

Returns:The param_name of this Parameters. Return type:str

param_value

Gets the param_value of this Parameters.

Returns:The param_value of this Parameters. Return type:str

to_dict()

Returns the model properties as a dict

to_str()

Returns the string representation of the model

sdi.models.pattern module

SDI - Semantic Data Interconnect APIs

The Semantic Data Interconnect (SDI) is a collection of APIs that allows the user to unlock the potential of disparate big data by connecting external data. The SDI can infer the schemas of data based on schema-on-read, allow creating a semantic model and perform big data semantic queries. It seamlessly connects to MindSphere’s Integrated Data Lake (IDL), but it can work independently as well. There are two mechanisms that can be used to upload files so that SDI can generate schemas and make data ready for query. The SDI operations are divided into the following groups: Data Registration for SDI This set of APIs is used to organize the incoming data. When configuring a Data Registry, you have the option to update your data based on a replace or append strategy. If you consider a use case where schema may change and incoming data files are completely different every time then replace is a good strategy. The replace strategy will replace the existing schema and data during each data ingest operation whereas the append strategy will update the existing schema and data during each data ingest operation. Custom Data Type for SDI The SDI by default identifies basic data types for each property, such as String, Integer, Float, Date, etc. The user can use this set of APIs to create their own custom data type. The SDI also provides an API under this category to suggest data type based on user-provided sample test values. Data Lake for SDI The SDI can process files uploaded provides endpoints to manage customer’s data lake registration based on tenant id, cloud provider and data lake type. The set of REST endpoint allows to create, update and retrieve base path for their data lake. The IDL customer needs to create an SDI folder that is under the root folder. Any file uploaded in this folder is automatically picked up by SDI to process via IDL notification. Data Ingest for SDI This set of APIs allows user to upload files, start an ingest job for uploaded files, find job status for ingested jobs or retrieve all job statuses. Schema Registry for SDI The SDI provides a way to find the generated schema in this category. Users can find an SDI generated schema for uploaded files based on source name, data tag or schema name. Data Query for SDI allows querying based on the extracted schemas. Important supported APIs are: * Query interface for querying semantically correlated and transformed data. * Stores and executes data queries. * Uses a semantic model to translate model-based query to physical queries. Semantic Model for SDI allows user to create semantic model ontologies based on the extracted one or more schemas. The important functionalities achieved with APIs are: * Contextual correlation of data from different systems. * Infers & Recommends mappings between different schemas. * Import and store Semantic model. # noqa: E501

class Pattern(schema=None, matches=None, schema_valid=None)

Bases: object

Attributes:

attribute_types (dict): The key is attribute name

and the value is attribute type.

attribute_map (dict): The key is attribute name

and the value is json key in definition.

attribute_map = {'matches': 'matches', 'schema': 'schema', 'schema_valid': 'schemaValid'}

attribute_types = {'matches': 'list[str]', 'schema': 'str', 'schema_valid': 'bool'}

matches

Gets the matches of this Pattern.

Returns:The matches of this Pattern. Return type:list[str]

schema

Gets the schema of this Pattern.

Returns:The schema of this Pattern. Return type:str

schema_valid

Gets the schema_valid of this Pattern.

Returns:The schema_valid of this Pattern. Return type:bool

to_dict()

Returns the model properties as a dict

to_str()

Returns the string representation of the model

sdi.models.query_obsolete_result module

SDI - Semantic Data Interconnect APIs

The Semantic Data Interconnect (SDI) is a collection of APIs that allows the user to unlock the potential of disparate big data by connecting external data. The SDI can infer the schemas of data based on schema-on-read, allow creating a semantic model and perform big data semantic queries. It seamlessly connects to MindSphere’s Integrated Data Lake (IDL), but it can work independently as well. There are two mechanisms that can be used to upload files so that SDI can generate schemas and make data ready for query. The SDI operations are divided into the following groups: Data Registration for SDI This set of APIs is used to organize the incoming data. When configuring a Data Registry, you have the option to update your data based on a replace or append strategy. If you consider a use case where schema may change and incoming data files are completely different every time then replace is a good strategy. The replace strategy will replace the existing schema and data during each data ingest operation whereas the append strategy will update the existing schema and data during each data ingest operation. Custom Data Type for SDI The SDI by default identifies basic data types for each property, such as String, Integer, Float, Date, etc. The user can use this set of APIs to create their own custom data type. The SDI also provides an API under this category to suggest data type based on user-provided sample test values. Data Lake for SDI The SDI can process files uploaded provides endpoints to manage customer’s data lake registration based on tenant id, cloud provider and data lake type. The set of REST endpoint allows to create, update and retrieve base path for their data lake. The IDL customer needs to create an SDI folder that is under the root folder. Any file uploaded in this folder is automatically picked up by SDI to process via IDL notification. Data Ingest for SDI This set of APIs allows user to upload files, start an ingest job for uploaded files, find job status for ingested jobs or retrieve all job statuses. Schema Registry for SDI The SDI provides a way to find the generated schema in this category. Users can find an SDI generated schema for uploaded files based on source name, data tag or schema name. Data Query for SDI allows querying based on the extracted schemas. Important supported APIs are: * Query interface for querying semantically correlated and transformed data. * Stores and executes data queries. * Uses a semantic model to translate model-based query to physical queries. Semantic Model for SDI allows user to create semantic model ontologies based on the extracted one or more schemas. The important functionalities achieved with APIs are: * Contextual correlation of data from different systems. * Infers & Recommends mappings between different schemas. * Import and store Semantic model. # noqa: E501

class QueryObsoleteResult(errors=None, data=None)

Bases: object

Attributes:

attribute_types (dict): The key is attribute name

and the value is attribute type.

attribute_map (dict): The key is attribute name

and the value is json key in definition.

attribute_map = {'data': 'data', 'errors': 'errors'}

attribute_types = {'data': 'list[ERRORUNKNOWN]', 'errors': 'list[ApiFieldError]'}

data

Gets the data of this QueryObsoleteResult.

Returns:The data of this QueryObsoleteResult. Return type:list[ERRORUNKNOWN]

errors

Gets the errors of this QueryObsoleteResult.

Returns:The errors of this QueryObsoleteResult. Return type:list[ApiFieldError]

to_dict()

Returns the model properties as a dict

to_str()

Returns the string representation of the model

sdi.models.query_parameters module

SDI - Semantic Data Interconnect APIs

The Semantic Data Interconnect (SDI) is a collection of APIs that allows the user to unlock the potential of disparate big data by connecting external data. The SDI can infer the schemas of data based on schema-on-read, allow creating a semantic model and perform big data semantic queries. It seamlessly connects to MindSphere’s Integrated Data Lake (IDL), but it can work independently as well. There are two mechanisms that can be used to upload files so that SDI can generate schemas and make data ready for query. The SDI operations are divided into the following groups: Data Registration for SDI This set of APIs is used to organize the incoming data. When configuring a Data Registry, you have the option to update your data based on a replace or append strategy. If you consider a use case where schema may change and incoming data files are completely different every time then replace is a good strategy. The replace strategy will replace the existing schema and data during each data ingest operation whereas the append strategy will update the existing schema and data during each data ingest operation. Custom Data Type for SDI The SDI by default identifies basic data types for each property, such as String, Integer, Float, Date, etc. The user can use this set of APIs to create their own custom data type. The SDI also provides an API under this category to suggest data type based on user-provided sample test values. Data Lake for SDI The SDI can process files uploaded provides endpoints to manage customer’s data lake registration based on tenant id, cloud provider and data lake type. The set of REST endpoint allows to create, update and retrieve base path for their data lake. The IDL customer needs to create an SDI folder that is under the root folder. Any file uploaded in this folder is automatically picked up by SDI to process via IDL notification. Data Ingest for SDI This set of APIs allows user to upload files, start an ingest job for uploaded files, find job status for ingested jobs or retrieve all job statuses. Schema Registry for SDI The SDI provides a way to find the generated schema in this category. Users can find an SDI generated schema for uploaded files based on source name, data tag or schema name. Data Query for SDI allows querying based on the extracted schemas. Important supported APIs are: * Query interface for querying semantically correlated and transformed data. * Stores and executes data queries. * Uses a semantic model to translate model-based query to physical queries. Semantic Model for SDI allows user to create semantic model ontologies based on the extracted one or more schemas. The important functionalities achieved with APIs are: * Contextual correlation of data from different systems. * Infers & Recommends mappings between different schemas. * Import and store Semantic model. # noqa: E501

class QueryParameters(data_tag=None, source_name=None, page_token=None, query_id=None, status=None, executable=None, is_dynamic=None, ontology_id=None, filter=None)

Bases: object

Attributes:

attribute_types (dict): The key is attribute name

and the value is attribute type.

attribute_map (dict): The key is attribute name

and the value is json key in definition.

attribute_map = {'data_tag': 'data_tag', 'executable': 'executable', 'filter': 'filter', 'is_dynamic': 'is_dynamic', 'ontology_id': 'ontology_id', 'page_token': 'page_value', 'query_id': 'query_id', 'source_name': 'source_name', 'status': 'status'}

attribute_types = {'data_tag': 'str', 'executable': 'str', 'filter': 'filter', 'is_dynamic': 'str', 'ontology_id': 'ontology_id', 'page_token': 'str', 'query_id': 'str', 'source_name': 'str', 'status': 'str'}

data_tag

Gets the data_tag of this QueryParameters.

Returns:The data_tag of this QueryParameters. Return type:str

executable

Gets the executable of this QueryParameters.

Returns:The executable of this QueryParameters. Return type:str

filter

Gets the filter of this QueryParameters.

Returns:The filter of this QueryParameters. Return type:str

is_dynamic

Gets the is_dynamic of this QueryParameters.

Returns:The is_dynamic of this QueryParameters. Return type:str

ontology_id

Gets the ontology_id of this QueryParameters.

Returns:The ontology_id of this QueryParameters. Return type:str

page_token

Gets the page_token of this QueryParameters.

Returns:The page_token of this QueryParameters. Return type:str

query_id

Gets the query_id of this QueryParameters.

Returns:The query_id of this QueryParameters. Return type:str

source_name

Gets the source_name of this QueryParameters.

Returns:The source_name of this QueryParameters. Return type:str

status

Gets the status of this QueryParameters.

Returns:The status of this QueryParameters. Return type:str

to_dict()

Returns the model properties as a dict

to_str()

Returns the string representation of the model

sdi.models.query_result module

SDI - Semantic Data Interconnect APIs

The Semantic Data Interconnect (SDI) is a collection of APIs that allows the user to unlock the potential of disparate big data by connecting external data. The SDI can infer the schemas of data based on schema-on-read, allow creating a semantic model and perform big data semantic queries. It seamlessly connects to MindSphere’s Integrated Data Lake (IDL), but it can work independently as well. There are two mechanisms that can be used to upload files so that SDI can generate schemas and make data ready for query. The SDI operations are divided into the following groups: Data Registration for SDI This set of APIs is used to organize the incoming data. When configuring a Data Registry, you have the option to update your data based on a replace or append strategy. If you consider a use case where schema may change and incoming data files are completely different every time then replace is a good strategy. The replace strategy will replace the existing schema and data during each data ingest operation whereas the append strategy will update the existing schema and data during each data ingest operation. Custom Data Type for SDI The SDI by default identifies basic data types for each property, such as String, Integer, Float, Date, etc. The user can use this set of APIs to create their own custom data type. The SDI also provides an API under this category to suggest data type based on user-provided sample test values. Data Lake for SDI The SDI can process files uploaded provides endpoints to manage customer’s data lake registration based on tenant id, cloud provider and data lake type. The set of REST endpoint allows to create, update and retrieve base path for their data lake. The IDL customer needs to create an SDI folder that is under the root folder. Any file uploaded in this folder is automatically picked up by SDI to process via IDL notification. Data Ingest for SDI This set of APIs allows user to upload files, start an ingest job for uploaded files, find job status for ingested jobs or retrieve all job statuses. Schema Registry for SDI The SDI provides a way to find the generated schema in this category. Users can find an SDI generated schema for uploaded files based on source name, data tag or schema name. Data Query for SDI allows querying based on the extracted schemas. Important supported APIs are: * Query interface for querying semantically correlated and transformed data. * Stores and executes data queries. * Uses a semantic model to translate model-based query to physical queries. Semantic Model for SDI allows user to create semantic model ontologies based on the extracted one or more schemas. The important functionalities achieved with APIs are: * Contextual correlation of data from different systems. * Infers & Recommends mappings between different schemas. * Import and store Semantic model. # noqa: E501

class QueryResult(status=None, timestamp=None, data=None)

Bases: object

Attributes:

attribute_types (dict): The key is attribute name

and the value is attribute type.

attribute_map (dict): The key is attribute name

and the value is json key in definition.

attribute_map = {'data': 'data', 'status': 'status', 'timestamp': 'timestamp'}

attribute_types = {'data': 'list[ERRORUNKNOWN]', 'status': 'str', 'timestamp': 'str'}

data

Gets the data of this QueryResult.

Returns:The data of this QueryResult. Return type:list[ERRORUNKNOWN]

status

Gets the status of this QueryResult. Query result status.

Returns:The status of this QueryResult. Return type:str

timestamp

Gets the timestamp of this QueryResult.

Returns:The timestamp of this QueryResult. Return type:str

to_dict()

Returns the model properties as a dict

to_str()

Returns the string representation of the model

sdi.models.response_all_data_query_execution_response module

SDI - Semantic Data Interconnect APIs

The Semantic Data Interconnect (SDI) is a collection of APIs that allows the user to unlock the potential of disparate big data by connecting external data. The SDI can infer the schemas of data based on schema-on-read, allow creating a semantic model and perform big data semantic queries. It seamlessly connects to MindSphere’s Integrated Data Lake (IDL), but it can work independently as well. There are two mechanisms that can be used to upload files so that SDI can generate schemas and make data ready for query. The SDI operations are divided into the following groups: Data Registration for SDI This set of APIs is used to organize the incoming data. When configuring a Data Registry, you have the option to update your data based on a replace or append strategy. If you consider a use case where schema may change and incoming data files are completely different every time then replace is a good strategy. The replace strategy will replace the existing schema and data during each data ingest operation whereas the append strategy will update the existing schema and data during each data ingest operation. Custom Data Type for SDI The SDI by default identifies basic data types for each property, such as String, Integer, Float, Date, etc. The user can use this set of APIs to create their own custom data type. The SDI also provides an API under this category to suggest data type based on user-provided sample test values. Data Lake for SDI The SDI can process files uploaded provides endpoints to manage customer’s data lake registration based on tenant id, cloud provider and data lake type. The set of REST endpoint allows to create, update and retrieve base path for their data lake. The IDL customer needs to create an SDI folder that is under the root folder. Any file uploaded in this folder is automatically picked up by SDI to process via IDL notification. Data Ingest for SDI This set of APIs allows user to upload files, start an ingest job for uploaded files, find job status for ingested jobs or retrieve all job statuses. Schema Registry for SDI The SDI provides a way to find the generated schema in this category. Users can find an SDI generated schema for uploaded files based on source name, data tag or schema name. Data Query for SDI allows querying based on the extracted schemas. Important supported APIs are: * Query interface for querying semantically correlated and transformed data. * Stores and executes data queries. * Uses a semantic model to translate model-based query to physical queries. Semantic Model for SDI allows user to create semantic model ontologies based on the extracted one or more schemas. The important functionalities achieved with APIs are: * Contextual correlation of data from different systems. * Infers & Recommends mappings between different schemas. * Import and store Semantic model. # noqa: E501

class ResponseAllDataQueryExecutionResponse(page=None, jobs=None)

Bases: object

Attributes:

attribute_types (dict): The key is attribute name

and the value is attribute type.

attribute_map (dict): The key is attribute name

and the value is json key in definition.

attribute_map = {'jobs': 'jobs', 'page': 'page'}

attribute_types = {'jobs': 'list[DataQueryExecutionResponse]', 'page': 'TokenPage'}

jobs

Gets the jobs of this ResponseAllDataQueryExecutionResponse.

Returns:The jobs of this ResponseAllDataQueryExecutionResponse. Return type:list[DataQueryExecutionResponse]

page

Gets the page of this ResponseAllDataQueryExecutionResponse.

Returns:The page of this ResponseAllDataQueryExecutionResponse. Return type:TokenPage

to_dict()

Returns the model properties as a dict

to_str()

Returns the string representation of the model

sdi.models.response_all_data_sql_query module

SDI - Semantic Data Interconnect APIs

The Semantic Data Interconnect (SDI) is a collection of APIs that allows the user to unlock the potential of disparate big data by connecting external data. The SDI can infer the schemas of data based on schema-on-read, allow creating a semantic model and perform big data semantic queries. It seamlessly connects to MindSphere’s Integrated Data Lake (IDL), but it can work independently as well. There are two mechanisms that can be used to upload files so that SDI can generate schemas and make data ready for query. The SDI operations are divided into the following groups: Data Registration for SDI This set of APIs is used to organize the incoming data. When configuring a Data Registry, you have the option to update your data based on a replace or append strategy. If you consider a use case where schema may change and incoming data files are completely different every time then replace is a good strategy. The replace strategy will replace the existing schema and data during each data ingest operation whereas the append strategy will update the existing schema and data during each data ingest operation. Custom Data Type for SDI The SDI by default identifies basic data types for each property, such as String, Integer, Float, Date, etc. The user can use this set of APIs to create their own custom data type. The SDI also provides an API under this category to suggest data type based on user-provided sample test values. Data Lake for SDI The SDI can process files uploaded provides endpoints to manage customer’s data lake registration based on tenant id, cloud provider and data lake type. The set of REST endpoint allows to create, update and retrieve base path for their data lake. The IDL customer needs to create an SDI folder that is under the root folder. Any file uploaded in this folder is automatically picked up by SDI to process via IDL notification. Data Ingest for SDI This set of APIs allows user to upload files, start an ingest job for uploaded files, find job status for ingested jobs or retrieve all job statuses. Schema Registry for SDI The SDI provides a way to find the generated schema in this category. Users can find an SDI generated schema for uploaded files based on source name, data tag or schema name. Data Query for SDI allows querying based on the extracted schemas. Important supported APIs are: * Query interface for querying semantically correlated and transformed data. * Stores and executes data queries. * Uses a semantic model to translate model-based query to physical queries. Semantic Model for SDI allows user to create semantic model ontologies based on the extracted one or more schemas. The important functionalities achieved with APIs are: * Contextual correlation of data from different systems. * Infers & Recommends mappings between different schemas. * Import and store Semantic model. # noqa: E501

class ResponseAllDataSQLQuery(page=None, queries=None)

Bases: object

Attributes:

attribute_types (dict): The key is attribute name

and the value is attribute type.

attribute_map (dict): The key is attribute name

and the value is json key in definition.

attribute_map = {'page': 'page', 'queries': 'queries'}

attribute_types = {'page': 'TokenPage', 'queries': 'list[GetAllSQLQueriesData]'}

page

Gets the page of this ResponseAllDataSQLQuery.

Returns:The page of this ResponseAllDataSQLQuery. Return type:TokenPage

queries

Gets the queries of this ResponseAllDataSQLQuery.

Returns:The queries of this ResponseAllDataSQLQuery. Return type:list[GetAllSQLQueriesData]

to_dict()

Returns the model properties as a dict

to_str()

Returns the string representation of the model

sdi.models.response_all_ontologies module

SDI - Semantic Data Interconnect APIs

The Semantic Data Interconnect (SDI) is a collection of APIs that allows the user to unlock the potential of disparate big data by connecting external data. The SDI can infer the schemas of data based on schema-on-read, allow creating a semantic model and perform big data semantic queries. It seamlessly connects to MindSphere’s Integrated Data Lake (IDL), but it can work independently as well. There are two mechanisms that can be used to upload files so that SDI can generate schemas and make data ready for query. The SDI operations are divided into the following groups: Data Registration for SDI This set of APIs is used to organize the incoming data. When configuring a Data Registry, you have the option to update your data based on a replace or append strategy. If you consider a use case where schema may change and incoming data files are completely different every time then replace is a good strategy. The replace strategy will replace the existing schema and data during each data ingest operation whereas the append strategy will update the existing schema and data during each data ingest operation. Custom Data Type for SDI The SDI by default identifies basic data types for each property, such as String, Integer, Float, Date, etc. The user can use this set of APIs to create their own custom data type. The SDI also provides an API under this category to suggest data type based on user-provided sample test values. Data Lake for SDI The SDI can process files uploaded provides endpoints to manage customer’s data lake registration based on tenant id, cloud provider and data lake type. The set of REST endpoint allows to create, update and retrieve base path for their data lake. The IDL customer needs to create an SDI folder that is under the root folder. Any file uploaded in this folder is automatically picked up by SDI to process via IDL notification. Data Ingest for SDI This set of APIs allows user to upload files, start an ingest job for uploaded files, find job status for ingested jobs or retrieve all job statuses. Schema Registry for SDI The SDI provides a way to find the generated schema in this category. Users can find an SDI generated schema for uploaded files based on source name, data tag or schema name. Data Query for SDI allows querying based on the extracted schemas. Important supported APIs are: * Query interface for querying semantically correlated and transformed data. * Stores and executes data queries. * Uses a semantic model to translate model-based query to physical queries. Semantic Model for SDI allows user to create semantic model ontologies based on the extracted one or more schemas. The important functionalities achieved with APIs are: * Contextual correlation of data from different systems. * Infers & Recommends mappings between different schemas. * Import and store Semantic model. # noqa: E501

class ResponseAllOntologies(ontologies=None, page=None)

Bases: object

Attributes:

attribute_types (dict): The key is attribute name

and the value is attribute type.

attribute_map (dict): The key is attribute name

and the value is json key in definition.

attribute_map = {'ontologies': 'ontologies', 'page': 'page'}

attribute_types = {'ontologies': 'list[OntologyMetadata]', 'page': 'TokenPage'}

ontologies

Gets the ontologies of this ResponseAllOntologies.

Returns:The ontologies of this ResponseAllOntologies. Return type:list[OntologyMetadata]

page

Gets the page of this ResponseAllOntologies.

Returns:The page of this ResponseAllOntologies. Return type:TokenPage

to_dict()

Returns the model properties as a dict

to_str()

Returns the string representation of the model

sdi.models.schema_search_object module

SDI - Semantic Data Interconnect APIs

The Semantic Data Interconnect (SDI) is a collection of APIs that allows the user to unlock the potential of disparate big data by connecting external data. The SDI can infer the schemas of data based on schema-on-read, allow creating a semantic model and perform big data semantic queries. It seamlessly connects to MindSphere’s Integrated Data Lake (IDL), but it can work independently as well. There are two mechanisms that can be used to upload files so that SDI can generate schemas and make data ready for query. The SDI operations are divided into the following groups: Data Registration for SDI This set of APIs is used to organize the incoming data. When configuring a Data Registry, you have the option to update your data based on a replace or append strategy. If you consider a use case where schema may change and incoming data files are completely different every time then replace is a good strategy. The replace strategy will replace the existing schema and data during each data ingest operation whereas the append strategy will update the existing schema and data during each data ingest operation. Custom Data Type for SDI The SDI by default identifies basic data types for each property, such as String, Integer, Float, Date, etc. The user can use this set of APIs to create their own custom data type. The SDI also provides an API under this category to suggest data type based on user-provided sample test values. Data Lake for SDI The SDI can process files uploaded provides endpoints to manage customer’s data lake registration based on tenant id, cloud provider and data lake type. The set of REST endpoint allows to create, update and retrieve base path for their data lake. The IDL customer needs to create an SDI folder that is under the root folder. Any file uploaded in this folder is automatically picked up by SDI to process via IDL notification. Data Ingest for SDI This set of APIs allows user to upload files, start an ingest job for uploaded files, find job status for ingested jobs or retrieve all job statuses. Schema Registry for SDI The SDI provides a way to find the generated schema in this category. Users can find an SDI generated schema for uploaded files based on source name, data tag or schema name. Data Query for SDI allows querying based on the extracted schemas. Important supported APIs are: * Query interface for querying semantically correlated and transformed data. * Stores and executes data queries. * Uses a semantic model to translate model-based query to physical queries. Semantic Model for SDI allows user to create semantic model ontologies based on the extracted one or more schemas. The important functionalities achieved with APIs are: * Contextual correlation of data from different systems. * Infers & Recommends mappings between different schemas. * Import and store Semantic model. # noqa: E501

class SchemaSearchObject(data_tag=None, schema_name=None, category=None, aspect_name=None, asset_id=None, source_name=None, meta_data_tags=None)

Bases: object

Attributes:

attribute_types (dict): The key is attribute name

and the value is attribute type.

attribute_map (dict): The key is attribute name

and the value is json key in definition.

aspect_name

Gets the aspect_name of this SchemaSearchObject.

Returns:The aspect_name of this SchemaSearchObject. Return type:str

asset_id

Gets the asset_id of this SchemaSearchObject.

Returns:The asset_id of this SchemaSearchObject. Return type:str

attribute_map = {'aspect_name': 'aspectName', 'asset_id': 'assetId', 'category': 'category', 'data_tag': 'dataTag', 'meta_data_tags': 'metaDataTags', 'schema_name': 'schemaName', 'source_name': 'sourceName'}

attribute_types = {'aspect_name': 'str', 'asset_id': 'str', 'category': 'str', 'data_tag': 'str', 'meta_data_tags': 'list[str]', 'schema_name': 'str', 'source_name': 'str'}

category

Gets the category of this SchemaSearchObject.

Returns:The category of this SchemaSearchObject. Return type:str

data_tag

Gets the data_tag of this SchemaSearchObject.

Returns:The data_tag of this SchemaSearchObject. Return type:str

meta_data_tags

Gets the meta_data_tags of this SchemaSearchObject. metaDataTags can be defined while creating a data registry.

Returns:The meta_data_tags of this SchemaSearchObject. Return type:list[str]

schema_name

Gets the schema_name of this SchemaSearchObject.

Returns:The schema_name of this SchemaSearchObject. Return type:str

source_name

Gets the source_name of this SchemaSearchObject.

Returns:The source_name of this SchemaSearchObject. Return type:str

to_dict()

Returns the model properties as a dict

to_str()

Returns the string representation of the model

sdi.models.schema_search_request module

SDI - Semantic Data Interconnect APIs

The Semantic Data Interconnect (SDI) is a collection of APIs that allows the user to unlock the potential of disparate big data by connecting external data. The SDI can infer the schemas of data based on schema-on-read, allow creating a semantic model and perform big data semantic queries. It seamlessly connects to MindSphere’s Integrated Data Lake (IDL), but it can work independently as well. There are two mechanisms that can be used to upload files so that SDI can generate schemas and make data ready for query. The SDI operations are divided into the following groups: Data Registration for SDI This set of APIs is used to organize the incoming data. When configuring a Data Registry, you have the option to update your data based on a replace or append strategy. If you consider a use case where schema may change and incoming data files are completely different every time then replace is a good strategy. The replace strategy will replace the existing schema and data during each data ingest operation whereas the append strategy will update the existing schema and data during each data ingest operation. Custom Data Type for SDI The SDI by default identifies basic data types for each property, such as String, Integer, Float, Date, etc. The user can use this set of APIs to create their own custom data type. The SDI also provides an API under this category to suggest data type based on user-provided sample test values. Data Lake for SDI The SDI can process files uploaded provides endpoints to manage customer’s data lake registration based on tenant id, cloud provider and data lake type. The set of REST endpoint allows to create, update and retrieve base path for their data lake. The IDL customer needs to create an SDI folder that is under the root folder. Any file uploaded in this folder is automatically picked up by SDI to process via IDL notification. Data Ingest for SDI This set of APIs allows user to upload files, start an ingest job for uploaded files, find job status for ingested jobs or retrieve all job statuses. Schema Registry for SDI The SDI provides a way to find the generated schema in this category. Users can find an SDI generated schema for uploaded files based on source name, data tag or schema name. Data Query for SDI allows querying based on the extracted schemas. Important supported APIs are: * Query interface for querying semantically correlated and transformed data. * Stores and executes data queries. * Uses a semantic model to translate model-based query to physical queries. Semantic Model for SDI allows user to create semantic model ontologies based on the extracted one or more schemas. The important functionalities achieved with APIs are: * Contextual correlation of data from different systems. * Infers & Recommends mappings between different schemas. * Import and store Semantic model. # noqa: E501

class SchemaSearchRequest(schemas=None)

Bases: object

Attributes:

attribute_types (dict): The key is attribute name

and the value is attribute type.

attribute_map (dict): The key is attribute name

and the value is json key in definition.

attribute_map = {'schemas': 'schemas'}

attribute_types = {'schemas': 'list[SchemaSearchObject]'}

schemas

Gets the schemas of this SchemaSearchRequest.

Returns:The schemas of this SchemaSearchRequest. Return type:list[SchemaSearchObject]

to_dict()

Returns the model properties as a dict

to_str()

Returns the string representation of the model

sdi.models.sdi_file_upload_response module

SDI - Semantic Data Interconnect APIs

The Semantic Data Interconnect (SDI) is a collection of APIs that allows the user to unlock the potential of disparate big data by connecting external data. The SDI can infer the schemas of data based on schema-on-read, allow creating a semantic model and perform big data semantic queries. It seamlessly connects to MindSphere’s Integrated Data Lake (IDL), but it can work independently as well. There are two mechanisms that can be used to upload files so that SDI can generate schemas and make data ready for query. The SDI operations are divided into the following groups: Data Registration for SDI This set of APIs is used to organize the incoming data. When configuring a Data Registry, you have the option to update your data based on a replace or append strategy. If you consider a use case where schema may change and incoming data files are completely different every time then replace is a good strategy. The replace strategy will replace the existing schema and data during each data ingest operation whereas the append strategy will update the existing schema and data during each data ingest operation. Custom Data Type for SDI The SDI by default identifies basic data types for each property, such as String, Integer, Float, Date, etc. The user can use this set of APIs to create their own custom data type. The SDI also provides an API under this category to suggest data type based on user-provided sample test values. Data Lake for SDI The SDI can process files uploaded provides endpoints to manage customer’s data lake registration based on tenant id, cloud provider and data lake type. The set of REST endpoint allows to create, update and retrieve base path for their data lake. The IDL customer needs to create an SDI folder that is under the root folder. Any file uploaded in this folder is automatically picked up by SDI to process via IDL notification. Data Ingest for SDI This set of APIs allows user to upload files, start an ingest job for uploaded files, find job status for ingested jobs or retrieve all job statuses. Schema Registry for SDI The SDI provides a way to find the generated schema in this category. Users can find an SDI generated schema for uploaded files based on source name, data tag or schema name. Data Query for SDI allows querying based on the extracted schemas. Important supported APIs are: * Query interface for querying semantically correlated and transformed data. * Stores and executes data queries. * Uses a semantic model to translate model-based query to physical queries. Semantic Model for SDI allows user to create semantic model ontologies based on the extracted one or more schemas. The important functionalities achieved with APIs are: * Contextual correlation of data from different systems. * Infers & Recommends mappings between different schemas. * Import and store Semantic model. # noqa: E501

class SdiFileUploadResponse(file_path=None)

Bases: object

Attributes:

attribute_types (dict): The key is attribute name

and the value is attribute type.

attribute_map (dict): The key is attribute name

and the value is json key in definition.

attribute_map = {'file_path': 'filePath'}

attribute_types = {'file_path': 'str'}

file_path

Gets the file_path of this SdiFileUploadResponse.

Returns:The file_path of this SdiFileUploadResponse. Return type:str

to_dict()

Returns the model properties as a dict

to_str()

Returns the string representation of the model

sdi.models.sdi_ingest_data module

SDI - Semantic Data Interconnect APIs

The Semantic Data Interconnect (SDI) is a collection of APIs that allows the user to unlock the potential of disparate big data by connecting external data. The SDI can infer the schemas of data based on schema-on-read, allow creating a semantic model and perform big data semantic queries. It seamlessly connects to MindSphere’s Integrated Data Lake (IDL), but it can work independently as well. There are two mechanisms that can be used to upload files so that SDI can generate schemas and make data ready for query. The SDI operations are divided into the following groups: Data Registration for SDI This set of APIs is used to organize the incoming data. When configuring a Data Registry, you have the option to update your data based on a replace or append strategy. If you consider a use case where schema may change and incoming data files are completely different every time then replace is a good strategy. The replace strategy will replace the existing schema and data during each data ingest operation whereas the append strategy will update the existing schema and data during each data ingest operation. Custom Data Type for SDI The SDI by default identifies basic data types for each property, such as String, Integer, Float, Date, etc. The user can use this set of APIs to create their own custom data type. The SDI also provides an API under this category to suggest data type based on user-provided sample test values. Data Lake for SDI The SDI can process files uploaded provides endpoints to manage customer’s data lake registration based on tenant id, cloud provider and data lake type. The set of REST endpoint allows to create, update and retrieve base path for their data lake. The IDL customer needs to create an SDI folder that is under the root folder. Any file uploaded in this folder is automatically picked up by SDI to process via IDL notification. Data Ingest for SDI This set of APIs allows user to upload files, start an ingest job for uploaded files, find job status for ingested jobs or retrieve all job statuses. Schema Registry for SDI The SDI provides a way to find the generated schema in this category. Users can find an SDI generated schema for uploaded files based on source name, data tag or schema name. Data Query for SDI allows querying based on the extracted schemas. Important supported APIs are: * Query interface for querying semantically correlated and transformed data. * Stores and executes data queries. * Uses a semantic model to translate model-based query to physical queries. Semantic Model for SDI allows user to create semantic model ontologies based on the extracted one or more schemas. The important functionalities achieved with APIs are: * Contextual correlation of data from different systems. * Infers & Recommends mappings between different schemas. * Import and store Semantic model. # noqa: E501

class SDIIngestData(data_tag=None, file_path=None, root_tag=None, source_name=None)

Bases: object

Attributes:

attribute_types (dict): The key is attribute name

and the value is attribute type.

attribute_map (dict): The key is attribute name

and the value is json key in definition.

attribute_map = {'data_tag': 'dataTag', 'file_path': 'filePath', 'root_tag': 'rootTag', 'source_name': 'sourceName'}

attribute_types = {'data_tag': 'str', 'file_path': 'str', 'root_tag': 'str', 'source_name': 'str'}

data_tag

Gets the data_tag of this SDIIngestData.

Returns:The data_tag of this SDIIngestData. Return type:str

file_path

Gets the file_path of this SDIIngestData.

Returns:The file_path of this SDIIngestData. Return type:str

root_tag

Gets the root_tag of this SDIIngestData.

Returns:The root_tag of this SDIIngestData. Return type:str

source_name

Gets the source_name of this SDIIngestData.

Returns:The source_name of this SDIIngestData. Return type:str

to_dict()

Returns the model properties as a dict

to_str()

Returns the string representation of the model

sdi.models.sdi_job_status_response module

SDI - Semantic Data Interconnect APIs

The Semantic Data Interconnect (SDI) is a collection of APIs that allows the user to unlock the potential of disparate big data by connecting external data. The SDI can infer the schemas of data based on schema-on-read, allow creating a semantic model and perform big data semantic queries. It seamlessly connects to MindSphere’s Integrated Data Lake (IDL), but it can work independently as well. There are two mechanisms that can be used to upload files so that SDI can generate schemas and make data ready for query. The SDI operations are divided into the following groups: Data Registration for SDI This set of APIs is used to organize the incoming data. When configuring a Data Registry, you have the option to update your data based on a replace or append strategy. If you consider a use case where schema may change and incoming data files are completely different every time then replace is a good strategy. The replace strategy will replace the existing schema and data during each data ingest operation whereas the append strategy will update the existing schema and data during each data ingest operation. Custom Data Type for SDI The SDI by default identifies basic data types for each property, such as String, Integer, Float, Date, etc. The user can use this set of APIs to create their own custom data type. The SDI also provides an API under this category to suggest data type based on user-provided sample test values. Data Lake for SDI The SDI can process files uploaded provides endpoints to manage customer’s data lake registration based on tenant id, cloud provider and data lake type. The set of REST endpoint allows to create, update and retrieve base path for their data lake. The IDL customer needs to create an SDI folder that is under the root folder. Any file uploaded in this folder is automatically picked up by SDI to process via IDL notification. Data Ingest for SDI This set of APIs allows user to upload files, start an ingest job for uploaded files, find job status for ingested jobs or retrieve all job statuses. Schema Registry for SDI The SDI provides a way to find the generated schema in this category. Users can find an SDI generated schema for uploaded files based on source name, data tag or schema name. Data Query for SDI allows querying based on the extracted schemas. Important supported APIs are: * Query interface for querying semantically correlated and transformed data. * Stores and executes data queries. * Uses a semantic model to translate model-based query to physical queries. Semantic Model for SDI allows user to create semantic model ontologies based on the extracted one or more schemas. The important functionalities achieved with APIs are: * Contextual correlation of data from different systems. * Infers & Recommends mappings between different schemas. * Import and store Semantic model. # noqa: E501

class SdiJobStatusResponse(job_id=None, started_date=None, finished_date=None, message=None, file_name=None, status=None)

Bases: object

Attributes:

attribute_types (dict): The key is attribute name

and the value is attribute type.

attribute_map (dict): The key is attribute name

and the value is json key in definition.

attribute_map = {'file_name': 'fileName', 'finished_date': 'finishedDate', 'job_id': 'jobId', 'message': 'message', 'started_date': 'startedDate', 'status': 'status'}

attribute_types = {'file_name': 'str', 'finished_date': 'str', 'job_id': 'str', 'message': 'str', 'started_date': 'str', 'status': 'str'}

file_name

Gets the file_name of this SdiJobStatusResponse.

Returns:The file_name of this SdiJobStatusResponse. Return type:str

finished_date

Gets the finished_date of this SdiJobStatusResponse.

Returns:The finished_date of this SdiJobStatusResponse. Return type:str

job_id

Gets the job_id of this SdiJobStatusResponse.

Returns:The job_id of this SdiJobStatusResponse. Return type:str

message

Gets the message of this SdiJobStatusResponse.

Returns:The message of this SdiJobStatusResponse. Return type:str

started_date

Gets the started_date of this SdiJobStatusResponse.

Returns:The started_date of this SdiJobStatusResponse. Return type:str

status

Gets the status of this SdiJobStatusResponse.

Returns:The status of this SdiJobStatusResponse. Return type:str

to_dict()

Returns the model properties as a dict

to_str()

Returns the string representation of the model

sdi.models.sdi_schema_property module

SDI - Semantic Data Interconnect APIs

The Semantic Data Interconnect (SDI) is a collection of APIs that allows the user to unlock the potential of disparate big data by connecting external data. The SDI can infer the schemas of data based on schema-on-read, allow creating a semantic model and perform big data semantic queries. It seamlessly connects to MindSphere’s Integrated Data Lake (IDL), but it can work independently as well. There are two mechanisms that can be used to upload files so that SDI can generate schemas and make data ready for query. The SDI operations are divided into the following groups: Data Registration for SDI This set of APIs is used to organize the incoming data. When configuring a Data Registry, you have the option to update your data based on a replace or append strategy. If you consider a use case where schema may change and incoming data files are completely different every time then replace is a good strategy. The replace strategy will replace the existing schema and data during each data ingest operation whereas the append strategy will update the existing schema and data during each data ingest operation. Custom Data Type for SDI The SDI by default identifies basic data types for each property, such as String, Integer, Float, Date, etc. The user can use this set of APIs to create their own custom data type. The SDI also provides an API under this category to suggest data type based on user-provided sample test values. Data Lake for SDI The SDI can process files uploaded provides endpoints to manage customer’s data lake registration based on tenant id, cloud provider and data lake type. The set of REST endpoint allows to create, update and retrieve base path for their data lake. The IDL customer needs to create an SDI folder that is under the root folder. Any file uploaded in this folder is automatically picked up by SDI to process via IDL notification. Data Ingest for SDI This set of APIs allows user to upload files, start an ingest job for uploaded files, find job status for ingested jobs or retrieve all job statuses. Schema Registry for SDI The SDI provides a way to find the generated schema in this category. Users can find an SDI generated schema for uploaded files based on source name, data tag or schema name. Data Query for SDI allows querying based on the extracted schemas. Important supported APIs are: * Query interface for querying semantically correlated and transformed data. * Stores and executes data queries. * Uses a semantic model to translate model-based query to physical queries. Semantic Model for SDI allows user to create semantic model ontologies based on the extracted one or more schemas. The important functionalities achieved with APIs are: * Contextual correlation of data from different systems. * Infers & Recommends mappings between different schemas. * Import and store Semantic model. # noqa: E501

class SDISchemaProperty(data_type=None, custom_types=None)

Bases: object

Attributes:

attribute_types (dict): The key is attribute name

and the value is attribute type.

attribute_map (dict): The key is attribute name

and the value is json key in definition.

attribute_map = {'custom_types': 'customTypes', 'data_type': 'dataType'}

attribute_types = {'custom_types': 'list[str]', 'data_type': 'str'}

custom_types

Gets the custom_types of this SDISchemaProperty.

Returns:The custom_types of this SDISchemaProperty. Return type:list[str]

data_type

Gets the data_type of this SDISchemaProperty.

Returns:The data_type of this SDISchemaProperty. Return type:str

to_dict()

Returns the model properties as a dict

to_str()

Returns the string representation of the model

sdi.models.sdi_schema_registry module

SDI - Semantic Data Interconnect APIs

The Semantic Data Interconnect (SDI) is a collection of APIs that allows the user to unlock the potential of disparate big data by connecting external data. The SDI can infer the schemas of data based on schema-on-read, allow creating a semantic model and perform big data semantic queries. It seamlessly connects to MindSphere’s Integrated Data Lake (IDL), but it can work independently as well. There are two mechanisms that can be used to upload files so that SDI can generate schemas and make data ready for query. The SDI operations are divided into the following groups: Data Registration for SDI This set of APIs is used to organize the incoming data. When configuring a Data Registry, you have the option to update your data based on a replace or append strategy. If you consider a use case where schema may change and incoming data files are completely different every time then replace is a good strategy. The replace strategy will replace the existing schema and data during each data ingest operation whereas the append strategy will update the existing schema and data during each data ingest operation. Custom Data Type for SDI The SDI by default identifies basic data types for each property, such as String, Integer, Float, Date, etc. The user can use this set of APIs to create their own custom data type. The SDI also provides an API under this category to suggest data type based on user-provided sample test values. Data Lake for SDI The SDI can process files uploaded provides endpoints to manage customer’s data lake registration based on tenant id, cloud provider and data lake type. The set of REST endpoint allows to create, update and retrieve base path for their data lake. The IDL customer needs to create an SDI folder that is under the root folder. Any file uploaded in this folder is automatically picked up by SDI to process via IDL notification. Data Ingest for SDI This set of APIs allows user to upload files, start an ingest job for uploaded files, find job status for ingested jobs or retrieve all job statuses. Schema Registry for SDI The SDI provides a way to find the generated schema in this category. Users can find an SDI generated schema for uploaded files based on source name, data tag or schema name. Data Query for SDI allows querying based on the extracted schemas. Important supported APIs are: * Query interface for querying semantically correlated and transformed data. * Stores and executes data queries. * Uses a semantic model to translate model-based query to physical queries. Semantic Model for SDI allows user to create semantic model ontologies based on the extracted one or more schemas. The important functionalities achieved with APIs are: * Contextual correlation of data from different systems. * Infers & Recommends mappings between different schemas. * Import and store Semantic model. # noqa: E501

class SDISchemaRegistry(created_date=None, id=None, updated_date=None, original_file_names=None, schema=None, schema_name=None, registry_id=None, category=None)

Bases: object

Attributes:

attribute_types (dict): The key is attribute name

and the value is attribute type.

attribute_map (dict): The key is attribute name

and the value is json key in definition.

attribute_map = {'category': 'category', 'created_date': 'createdDate', 'id': 'id', 'original_file_names': 'originalFileNames', 'registry_id': 'registryId', 'schema': 'schema', 'schema_name': 'schemaName', 'updated_date': 'updatedDate'}

attribute_types = {'category': 'str', 'created_date': 'str', 'id': 'str', 'original_file_names': 'list[str]', 'registry_id': 'str', 'schema': 'dict(str, ListOfSchemaProperties)', 'schema_name': 'str', 'updated_date': 'str'}

category

Gets the category of this SDISchemaRegistry.

Returns:The category of this SDISchemaRegistry. Return type:str

created_date

Gets the created_date of this SDISchemaRegistry.

Returns:The created_date of this SDISchemaRegistry. Return type:str

id

Gets the id of this SDISchemaRegistry.

Returns:The id of this SDISchemaRegistry. Return type:str

original_file_names

Gets the original_file_names of this SDISchemaRegistry.

Returns:The original_file_names of this SDISchemaRegistry. Return type:list[str]

registry_id

Gets the registry_id of this SDISchemaRegistry.

Returns:The registry_id of this SDISchemaRegistry. Return type:str

schema

Gets the schema of this SDISchemaRegistry. This is a loosely defined schema string containing property name, one or more data type for given property and list of regex patterns for a type.

Returns:The schema of this SDISchemaRegistry. Return type:dict(str, ListOfSchemaProperties)

schema_name

Gets the schema_name of this SDISchemaRegistry.

Returns:The schema_name of this SDISchemaRegistry. Return type:str

to_dict()

Returns the model properties as a dict

to_str()

Returns the string representation of the model

updated_date

Gets the updated_date of this SDISchemaRegistry.

Returns:The updated_date of this SDISchemaRegistry. Return type:str

sdi.models.suggest_patterns_post module

SDI - Semantic Data Interconnect APIs

The Semantic Data Interconnect (SDI) is a collection of APIs that allows the user to unlock the potential of disparate big data by connecting external data. The SDI can infer the schemas of data based on schema-on-read, allow creating a semantic model and perform big data semantic queries. It seamlessly connects to MindSphere’s Integrated Data Lake (IDL), but it can work independently as well. There are two mechanisms that can be used to upload files so that SDI can generate schemas and make data ready for query. The SDI operations are divided into the following groups: Data Registration for SDI This set of APIs is used to organize the incoming data. When configuring a Data Registry, you have the option to update your data based on a replace or append strategy. If you consider a use case where schema may change and incoming data files are completely different every time then replace is a good strategy. The replace strategy will replace the existing schema and data during each data ingest operation whereas the append strategy will update the existing schema and data during each data ingest operation. Custom Data Type for SDI The SDI by default identifies basic data types for each property, such as String, Integer, Float, Date, etc. The user can use this set of APIs to create their own custom data type. The SDI also provides an API under this category to suggest data type based on user-provided sample test values. Data Lake for SDI The SDI can process files uploaded provides endpoints to manage customer’s data lake registration based on tenant id, cloud provider and data lake type. The set of REST endpoint allows to create, update and retrieve base path for their data lake. The IDL customer needs to create an SDI folder that is under the root folder. Any file uploaded in this folder is automatically picked up by SDI to process via IDL notification. Data Ingest for SDI This set of APIs allows user to upload files, start an ingest job for uploaded files, find job status for ingested jobs or retrieve all job statuses. Schema Registry for SDI The SDI provides a way to find the generated schema in this category. Users can find an SDI generated schema for uploaded files based on source name, data tag or schema name. Data Query for SDI allows querying based on the extracted schemas. Important supported APIs are: * Query interface for querying semantically correlated and transformed data. * Stores and executes data queries. * Uses a semantic model to translate model-based query to physical queries. Semantic Model for SDI allows user to create semantic model ontologies based on the extracted one or more schemas. The important functionalities achieved with APIs are: * Contextual correlation of data from different systems. * Infers & Recommends mappings between different schemas. * Import and store Semantic model. # noqa: E501

class SuggestPatternsPostRequest(sample_values=None, test_values=None)

Bases: object

Attributes:

attribute_types (dict): The key is attribute name

and the value is attribute type.

attribute_map (dict): The key is attribute name

and the value is json key in definition.

attribute_map = {'sample_values': 'sample_values', 'test_values': 'test_values'}

attribute_types = {'sample_values': 'list[str]', 'test_values': 'list[str]'}

name

Gets the sample_values of this SuggestPatternsPostRequest.

Returns:The sample_values of this SuggestPatternsPostRequest. Return type:list[str]

patterns

Gets the test_values of this SuggestPatternsPostRequest.

Returns:The test_values of this SuggestPatternsPostRequest. Return type:list[str]

sample_values

Gets the sample_values of this SuggestPatternsPostRequest.

Returns:The sample_values of this SuggestPatternsPostRequest. Return type:list[str]

test_values

Gets the test_values of this SuggestPatternsPostRequest.

Returns:The test_values of this SuggestPatternsPostRequest. Return type:list[str]

to_dict()

Returns the model properties as a dict

to_str()

Returns the string representation of the model

sdi.models.token_page module

SDI - Semantic Data Interconnect APIs

The Semantic Data Interconnect (SDI) is a collection of APIs that allows the user to unlock the potential of disparate big data by connecting external data. The SDI can infer the schemas of data based on schema-on-read, allow creating a semantic model and perform big data semantic queries. It seamlessly connects to MindSphere’s Integrated Data Lake (IDL), but it can work independently as well. There are two mechanisms that can be used to upload files so that SDI can generate schemas and make data ready for query. The SDI operations are divided into the following groups: Data Registration for SDI This set of APIs is used to organize the incoming data. When configuring a Data Registry, you have the option to update your data based on a replace or append strategy. If you consider a use case where schema may change and incoming data files are completely different every time then replace is a good strategy. The replace strategy will replace the existing schema and data during each data ingest operation whereas the append strategy will update the existing schema and data during each data ingest operation. Custom Data Type for SDI The SDI by default identifies basic data types for each property, such as String, Integer, Float, Date, etc. The user can use this set of APIs to create their own custom data type. The SDI also provides an API under this category to suggest data type based on user-provided sample test values. Data Lake for SDI The SDI can process files uploaded provides endpoints to manage customer’s data lake registration based on tenant id, cloud provider and data lake type. The set of REST endpoint allows to create, update and retrieve base path for their data lake. The IDL customer needs to create an SDI folder that is under the root folder. Any file uploaded in this folder is automatically picked up by SDI to process via IDL notification. Data Ingest for SDI This set of APIs allows user to upload files, start an ingest job for uploaded files, find job status for ingested jobs or retrieve all job statuses. Schema Registry for SDI The SDI provides a way to find the generated schema in this category. Users can find an SDI generated schema for uploaded files based on source name, data tag or schema name. Data Query for SDI allows querying based on the extracted schemas. Important supported APIs are: * Query interface for querying semantically correlated and transformed data. * Stores and executes data queries. * Uses a semantic model to translate model-based query to physical queries. Semantic Model for SDI allows user to create semantic model ontologies based on the extracted one or more schemas. The important functionalities achieved with APIs are: * Contextual correlation of data from different systems. * Infers & Recommends mappings between different schemas. * Import and store Semantic model. # noqa: E501

class TokenPage(next_token=None)

Bases: object

Attributes:

attribute_types (dict): The key is attribute name

and the value is attribute type.

attribute_map (dict): The key is attribute name

and the value is json key in definition.

attribute_map = {'next_token': 'nextToken'}

attribute_types = {'next_token': 'str'}

next_token

Gets the next_token of this TokenPage. Opaque token to next page. Can be used in query paramter ‘pageToken’ to request next page. The property is only present in case there is a next page.

Returns:The next_token of this TokenPage. Return type:str

to_dict()

Returns the model properties as a dict

to_str()

Returns the string representation of the model

sdi.models.update_data_lake_request module

SDI - Semantic Data Interconnect APIs

The Semantic Data Interconnect (SDI) is a collection of APIs that allows the user to unlock the potential of disparate big data by connecting external data. The SDI can infer the schemas of data based on schema-on-read, allow creating a semantic model and perform big data semantic queries. It seamlessly connects to MindSphere’s Integrated Data Lake (IDL), but it can work independently as well. There are two mechanisms that can be used to upload files so that SDI can generate schemas and make data ready for query. The SDI operations are divided into the following groups: Data Registration for SDI This set of APIs is used to organize the incoming data. When configuring a Data Registry, you have the option to update your data based on a replace or append strategy. If you consider a use case where schema may change and incoming data files are completely different every time then replace is a good strategy. The replace strategy will replace the existing schema and data during each data ingest operation whereas the append strategy will update the existing schema and data during each data ingest operation. Custom Data Type for SDI The SDI by default identifies basic data types for each property, such as String, Integer, Float, Date, etc. The user can use this set of APIs to create their own custom data type. The SDI also provides an API under this category to suggest data type based on user-provided sample test values. Data Lake for SDI The SDI can process files uploaded provides endpoints to manage customer’s data lake registration based on tenant id, cloud provider and data lake type. The set of REST endpoint allows to create, update and retrieve base path for their data lake. The IDL customer needs to create an SDI folder that is under the root folder. Any file uploaded in this folder is automatically picked up by SDI to process via IDL notification. Data Ingest for SDI This set of APIs allows user to upload files, start an ingest job for uploaded files, find job status for ingested jobs or retrieve all job statuses. Schema Registry for SDI The SDI provides a way to find the generated schema in this category. Users can find an SDI generated schema for uploaded files based on source name, data tag or schema name. Data Query for SDI allows querying based on the extracted schemas. Important supported APIs are: * Query interface for querying semantically correlated and transformed data. * Stores and executes data queries. * Uses a semantic model to translate model-based query to physical queries. Semantic Model for SDI allows user to create semantic model ontologies based on the extracted one or more schemas. The important functionalities achieved with APIs are: * Contextual correlation of data from different systems. * Infers & Recommends mappings between different schemas. * Import and store Semantic model. # noqa: E501

class UpdateDataLakeRequest(base_path=None)

Bases: object

Attributes:

attribute_types (dict): The key is attribute name

and the value is attribute type.

attribute_map (dict): The key is attribute name

and the value is json key in definition.

attribute_map = {'base_path': 'basePath'}

attribute_types = {'base_path': 'str'}

base_path

Gets the base_path of this UpdateDataLakeRequest.

Returns:The base_path of this UpdateDataLakeRequest. Return type:str

to_dict()

Returns the model properties as a dict

to_str()

Returns the string representation of the model

sdi.models.update_data_registry_request module

SDI - Semantic Data Interconnect APIs

The Semantic Data Interconnect (SDI) is a collection of APIs that allows the user to unlock the potential of disparate big data by connecting external data. The SDI can infer the schemas of data based on schema-on-read, allow creating a semantic model and perform big data semantic queries. It seamlessly connects to MindSphere’s Integrated Data Lake (IDL), but it can work independently as well. There are two mechanisms that can be used to upload files so that SDI can generate schemas and make data ready for query. The SDI operations are divided into the following groups: Data Registration for SDI This set of APIs is used to organize the incoming data. When configuring a Data Registry, you have the option to update your data based on a replace or append strategy. If you consider a use case where schema may change and incoming data files are completely different every time then replace is a good strategy. The replace strategy will replace the existing schema and data during each data ingest operation whereas the append strategy will update the existing schema and data during each data ingest operation. Custom Data Type for SDI The SDI by default identifies basic data types for each property, such as String, Integer, Float, Date, etc. The user can use this set of APIs to create their own custom data type. The SDI also provides an API under this category to suggest data type based on user-provided sample test values. Data Lake for SDI The SDI can process files uploaded provides endpoints to manage customer’s data lake registration based on tenant id, cloud provider and data lake type. The set of REST endpoint allows to create, update and retrieve base path for their data lake. The IDL customer needs to create an SDI folder that is under the root folder. Any file uploaded in this folder is automatically picked up by SDI to process via IDL notification. Data Ingest for SDI This set of APIs allows user to upload files, start an ingest job for uploaded files, find job status for ingested jobs or retrieve all job statuses. Schema Registry for SDI The SDI provides a way to find the generated schema in this category. Users can find an SDI generated schema for uploaded files based on source name, data tag or schema name. Data Query for SDI allows querying based on the extracted schemas. Important supported APIs are: * Query interface for querying semantically correlated and transformed data. * Stores and executes data queries. * Uses a semantic model to translate model-based query to physical queries. Semantic Model for SDI allows user to create semantic model ontologies based on the extracted one or more schemas. The important functionalities achieved with APIs are: * Contextual correlation of data from different systems. * Infers & Recommends mappings between different schemas. * Import and store Semantic model. # noqa: E501

class UpdateDataRegistryRequest(default_root_tag=None, file_pattern=None, file_upload_strategy=None, meta_data_tags=None, xml_process_rules=None, schema_frozen=None)

Bases: object

Attributes:

attribute_types (dict): The key is attribute name

and the value is attribute type.

attribute_map (dict): The key is attribute name

and the value is json key in definition.

attribute_map = {'default_root_tag': 'defaultRootTag', 'file_pattern': 'filePattern', 'file_upload_strategy': 'fileUploadStrategy', 'meta_data_tags': 'metaDataTags', 'schema_frozen': 'schemaFrozen', 'xml_process_rules': 'xmlProcessRules'}

attribute_types = {'default_root_tag': 'str', 'file_pattern': 'str', 'file_upload_strategy': 'str', 'meta_data_tags': 'list[str]', 'schema_frozen': 'bool', 'xml_process_rules': 'list[str]'}

default_root_tag

Gets the default_root_tag of this UpdateDataRegistryRequest.

Returns:The default_root_tag of this UpdateDataRegistryRequest. Return type:str

file_pattern

Gets the file_pattern of this UpdateDataRegistryRequest.

Returns:The file_pattern of this UpdateDataRegistryRequest. Return type:str

file_upload_strategy

Gets the file_upload_strategy of this UpdateDataRegistryRequest.

Returns:The file_upload_strategy of this UpdateDataRegistryRequest. Return type:str

meta_data_tags

Gets the meta_data_tags of this UpdateDataRegistryRequest.

Returns:The meta_data_tags of this UpdateDataRegistryRequest. Return type:list[str]

schema_frozen

Gets the schema_frozen of this UpdateDataRegistryRequest. This property can be changed to true after creating the initial schema to reuse the schema for the newly ingested data

Returns:The schema_frozen of this UpdateDataRegistryRequest. Return type:bool

to_dict()

Returns the model properties as a dict

to_str()

Returns the string representation of the model

xml_process_rules

Gets the xml_process_rules of this UpdateDataRegistryRequest.

Returns:The xml_process_rules of this UpdateDataRegistryRequest. Return type:list[str]

Module contents

SDI - Semantic Data Interconnect APIs

The Semantic Data Interconnect (SDI) is a collection of APIs that allows the user to unlock the potential of disparate big data by connecting external data. The SDI can infer the schemas of data based on schema-on-read, allow creating a semantic model and perform big data semantic queries. It seamlessly connects to MindSphere’s Integrated Data Lake (IDL), but it can work independently as well. There are two mechanisms that can be used to upload files so that SDI can generate schemas and make data ready for query. The SDI operations are divided into the following groups: Data Registration for SDI This set of APIs is used to organize the incoming data. When configuring a Data Registry, you have the option to update your data based on a replace or append strategy. If you consider a use case where schema may change and incoming data files are completely different every time then replace is a good strategy. The replace strategy will replace the existing schema and data during each data ingest operation whereas the append strategy will update the existing schema and data during each data ingest operation. Custom Data Type for SDI The SDI by default identifies basic data types for each property, such as String, Integer, Float, Date, etc. The user can use this set of APIs to create their own custom data type. The SDI also provides an API under this category to suggest data type based on user-provided sample test values. Data Lake for SDI The SDI can process files uploaded provides endpoints to manage customer’s data lake registration based on tenant id, cloud provider and data lake type. The set of REST endpoint allows to create, update and retrieve base path for their data lake. The IDL customer needs to create an SDI folder that is under the root folder. Any file uploaded in this folder is automatically picked up by SDI to process via IDL notification. Data Ingest for SDI This set of APIs allows user to upload files, start an ingest job for uploaded files, find job status for ingested jobs or retrieve all job statuses. Schema Registry for SDI The SDI provides a way to find the generated schema in this category. Users can find an SDI generated schema for uploaded files based on source name, data tag or schema name. Data Query for SDI allows querying based on the extracted schemas. Important supported APIs are: * Query interface for querying semantically correlated and transformed data. * Stores and executes data queries. * Uses a semantic model to translate model-based query to physical queries. Semantic Model for SDI allows user to create semantic model ontologies based on the extracted one or more schemas. The important functionalities achieved with APIs are: * Contextual correlation of data from different systems. * Infers & Recommends mappings between different schemas. * Import and store Semantic model. # noqa: E501