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ConfiguredAssetSqlDataConnector

class great_expectations.datasource.data_connector.ConfiguredAssetSqlDataConnector(name: str, datasource_name: str, execution_engine: Optional[great_expectations.execution_engine.execution_engine.ExecutionEngine] = None, include_schema_name: bool = False, splitter_method: Optional[str] = None, splitter_kwargs: Optional[dict] = None, sorters: Optional[list] = None, sampling_method: Optional[str] = None, sampling_kwargs: Optional[dict] = None, assets: Optional[Dict[str, dict]] = None, batch_spec_passthrough: Optional[dict] = None, id: Optional[str] = None)#

A DataConnector that requires explicit listing of SQL assets you want to connect to.

Being a Configured Asset Data Connector, it requires an explicit list of each Data Asset it can connect to. While this allows for fine-grained control over which Data Assets may be accessed, it requires more setup.

Parameters:

  • name (str) – The name of this DataConnector

  • datasource_name (str) – The name of the Datasource that contains it

  • execution_engine (ExecutionEngine) – An ExecutionEngine

  • include_schema_name (bool) – Should the data_asset_name include the schema as a prefix?

  • splitter_method (str) – A method to split the target table into multiple Batches

  • splitter_kwargs (dict) – Keyword arguments to pass to splitter_method

  • sorters (list) – List if you want to override the default sort for the data_references

  • sampling_method (str) – A method to downsample within a target Batch

  • sampling_kwargs (dict) – Keyword arguments to pass to sampling_method

  • batch_spec_passthrough (dict) – dictionary with keys that will be added directly to batch_spec

get_available_data_asset_names() → List[str]#

Return the list of asset names known by this DataConnector.

Returns:

A list of available names