A Data Source provides a standard API for accessing and interacting with data from a wide variety of source systems.
Data Sources provide a standard API across multiple backends: the Data Source API remains the same for PostgreSQL, CSV Filesystems, and all other supported data backends.
Data Sources do not modify your data.
Relationship to other objects
Data Sources function by bringing together a way of interacting with Data (an Execution EngineA system capable of processing data to compute Metrics.) with a definition of the data to access (a Data Asset). Batch RequestsProvided to a Data Source in order to create a Batch. utilize a Data Sources' Data Assets to return a BatchA selection of records from a Data Asset. of data.
When connecting to data the Data Source is your primary tool. At this stage, you will create Data Sources to define how Great Expectations can find and access your Data AssetsA collection of records within a Data Source which is usually named based on the underlying data system and sliced to correspond to a desired specification.. Under the hood, each Data Source uses an Execution Engine (ex: SQLAlchemy, Pandas, and Spark) to connect to and query data. Once a Data Source is configured you will be able to operate with the Data Source's API rather than needing a different API for each possible data backend you may be working with.
When creating ExpectationsA verifiable assertion about data., you'll use your Data Sources to obtain BatchesA selection of records from a Data Asset. for analysis and for your Expectation SuitesA collection of verifiable assertions about data.. For example, when you use the interactive workflow to create new Expectations.
Data Sources are also used to obtain Batches for ValidatorsUsed to run an Expectation Suite against data. to run against when you are validating data.
Data Sources support connecting to a variety of different data backends. No matter which Data Source you use, the Data Source's API remains the same.
No unexpected modifications
Data Sources do not modify your data during profiling or validation, but they may create temporary artifacts to optimize computing Metrics and Validation (this behavior can be configured).
Create and access
Data Sources can be created and accessed using Python code, which can be executed from a script, a Python console, or a Jupyter Notebook. To access a Data Source all you need is a Data ContextThe primary entry point for a Great Expectations deployment, with configurations and methods for all supporting components. and the name of the Data Source. The below snippet shows how to create a Pandas Data Source for local files:
import great_expectations as gx
context = gx.get_context()
This next snippet shows how to retrieve the Data Source from the Data Context.
datasource = context.datasources["my_pandas_datasource"]
For detailed instructions on how to create Data Sources that are configured for various backends, see our documentation on Connecting to Data Sources.