Datasource Module

class great_expectations.datasource.Datasource(name, data_context=None, data_asset_type=None, generators=None, **kwargs)

Datasources are responsible for connecting data and compute infrastructure. Each Datasource provides Great Expectations DataAssets (or batches in a DataContext) connected to a specific compute environment, such as a SQL database, a Spark cluster, or a local in-memory Pandas DataFrame. Datasources know how to access data from relevant sources such as an existing object from a DAG runner, a SQL database, S3 bucket, or local filesystem.

To bridge the gap between those worlds, Datasources interact closely with generators which are aware of a source of data and can produce produce identifying information, called “batch_kwargs” that datasources can use to get individual batches of data. They add flexibility in how to obtain data such as with time-based partitioning, downsampling, or other techniques appropriate for the datasource.

For example, a generator could produce a SQL query that logically represents “rows in the Events table with a timestamp on February 7, 2012,” which a SqlAlchemyDatasource could use to materialize a SqlAlchemyDataset corresponding to that batch of data and ready for validation.

Since opinionated DAG managers such as airflow, dbt, prefect.io, dagster can also act as datasources and/or generators for a more generic datasource.

When adding custom expectations by subclassing an existing DataAsset type, use the data_asset_type parameter to configure the datasource to load and return DataAssets of the custom type.

classmethod from_configuration(**kwargs)

Build a new datasource from a configuration dictionary.

Parameters

**kwargs – configuration key-value pairs

Returns

the newly-created datasource

Return type

datasource (Datasource)

classmethod build_configuration(class_name, module_name='great_expectations.datasource', data_asset_type=None, generators=None, **kwargs)

Build a full configuration object for a datasource, potentially including generators with defaults.

Parameters
  • class_name – The name of the class for which to build the config

  • module_name – The name of the module in which the datasource class is located

  • data_asset_type – A ClassConfig dictionary

  • generators – Generator configuration dictionary

  • **kwargs – Additional kwargs to be part of the datasource constructor’s initialization

Returns

A complete datasource configuration.

property data_context

Property for attached DataContext

property name

Property for datasource name

get_config()

Get the current configuration.

Returns

datasource configuration dictionary

add_generator(name, generator_config, **kwargs)

Add a generator to the datasource.

Parameters
  • name (str) – the name of the new generator to add

  • generator_config – the configuration parameters to add to the datasource

  • kwargs – additional keyword arguments will be passed directly to the new generator’s constructor

Returns

generator (Generator)

get_generator(generator_name='default')

Get the (named) generator from a datasource)

Parameters

generator_name (str) – name of generator (default value is ‘default’)

Returns

generator (Generator)

list_generators()

List currently-configured generators for this datasource.

Returns

each dictionary includes “name” and “type” keys

Return type

List(dict)

get_batch(data_asset_name, expectation_suite_name, batch_kwargs, **kwargs)

Get a batch of data from the datasource.

If a DataContext is attached, then expectation_suite_name can be used to define an expectation suite to attach to the data_asset being fetched. Otherwise, the expectation suite will be empty.

If no batch_kwargs are specified, the next kwargs for the named data_asset will be fetched from the generator first.

Specific datasource types implement the internal _get_data_asset method to use appropriate batch_kwargs to construct and return GE data_asset objects.

Parameters
  • data_asset_name – the name of the data asset for which to fetch data.

  • expectation_suite_name – the name of the expectation suite to attach to the batch

  • batch_kwargs – dictionary of key-value pairs describing the batch to get, or a single identifier if that can be unambiguously translated to batch_kwargs

  • **kwargs – Additional key-value pairs to pass to the datasource, such as reader parameters

Returns

A data_asset consisting of the specified batch of data with the named expectation suite connected.

get_data_asset(generator_asset, generator_name=None, expectation_suite=None, batch_kwargs=None, **kwargs)

Get a DataAsset using a datasource. generator_asset and generator_name are required.

Parameters
  • generator_asset – The name of the asset as identified by the generator to return.

  • generator_name – The name of the configured generator to use.

  • expectation_suite – The expectation suite to attach to the data_asset

  • batch_kwargs – Additional batch_kwargs that can

  • **kwargs – Additional kwargs that can be used to supplement batch_kwargs

Returns

DataAsset

get_available_data_asset_names(generator_names=None)

Returns a dictionary of data_asset_names that the specified generator can provide. Note that some generators, such as the “no-op” in-memory generator may not be capable of describing specific named data assets, and some generators (such as filesystem glob generators) require the user to configure data asset names.

Parameters

generator_names – the generators for which to fetch available data asset names.

Returns

{
  generator_name: [ data_asset_1, data_asset_2, ... ]
  ...
}

Return type

dictionary consisting of sets of generator assets available for the specified generators

build_batch_kwargs(data_asset_name, *args, **kwargs)

Build batch kwargs for a requested data_asset. Try to use a generator where possible to support partitioning, but fall back to datasource-default behavior if the generator cannot be identified.

Parameters
  • data_asset_name – the data asset for which to build batch_kwargs; if a normalized name is provided, use the named generator.

  • *args – at most exactly one positional argument can be provided from which to build kwargs

  • **kwargs – additional keyword arguments to be used to build the batch_kwargs

Returns

A PandasDatasourceBatchKwargs object suitable for building a batch of data from this datasource

named_generator_build_batch_kwargs(generator_name, generator_asset, *args, **kwargs)

Use the named generator to build batch_kwargs

get_data_context()

Getter for the currently-configured data context.

PandasDatasource

class great_expectations.datasource.pandas_datasource.PandasDatasource(name='pandas', data_context=None, data_asset_type=None, generators=None, **kwargs)

Bases: great_expectations.datasource.datasource.Datasource

The PandasDatasource produces PandasDataset objects and supports generators capable of interacting with the local filesystem (the default subdir_reader generator), and from existing in-memory dataframes.

classmethod build_configuration(data_asset_type=None, generators=None, **kwargs)

Build a full configuration object for a datasource, potentially including generators with defaults.

Parameters
  • data_asset_type – A ClassConfig dictionary

  • generators – Generator configuration dictionary

  • **kwargs – Additional kwargs to be part of the datasource constructor’s initialization

Returns

A complete datasource configuration.

SqlAlchemyDatasource

class great_expectations.datasource.sqlalchemy_datasource.SqlAlchemyDatasource(name='default', data_context=None, data_asset_type=None, credentials=None, generators=None, **kwargs)

Bases: great_expectations.datasource.datasource.Datasource

A SqlAlchemyDatasource will provide data_assets converting batch_kwargs using the following rules:
  • if the batch_kwargs include a table key, the datasource will provide a dataset object connected to that table

  • if the batch_kwargs include a query key, the datasource will create a temporary table using that that query. The query can be parameterized according to the standard python Template engine, which uses $parameter, with additional kwargs passed to the get_batch method.

classmethod build_configuration(data_asset_type=None, generators=None, **kwargs)

Build a full configuration object for a datasource, potentially including generators with defaults.

Parameters
  • data_asset_type – A ClassConfig dictionary

  • generators – Generator configuration dictionary

  • **kwargs – Additional kwargs to be part of the datasource constructor’s initialization

Returns

A complete datasource configuration.

SparkDFDatasource

class great_expectations.datasource.sparkdf_datasource.SparkDFDatasource(name='default', data_context=None, data_asset_type=None, generators=None, **kwargs)

Bases: great_expectations.datasource.datasource.Datasource

The SparkDFDatasource produces SparkDFDatasets and supports generators capable of interacting with local filesystem (the default subdir_reader generator) and databricks notebooks.

classmethod build_configuration(data_asset_type=None, generators=None, **kwargs)

Build a full configuration object for a datasource, potentially including generators with defaults.

Parameters
  • data_asset_type – A ClassConfig dictionary

  • generators – Generator configuration dictionary

  • **kwargs – Additional kwargs to be part of the datasource constructor’s initialization

Returns

A complete datasource configuration.