Batch Kwargs Generators¶
Batch Kwargs are specific instructions for a Datasource about what data should be prepared as a “batch” for validation. The batch could be a specific database table, the most recent log file delivered to S3, or even a subset of one of those objects such as the first 10,000 rows.
A BatchKwargsGenerator builds those instructions for GE datasources by inspecting storage backends or data, or by maintaining configuration such as commonly-used paths or filepath conventions. That allows BatchKwargsGenerators to add flexibility in how to obtain data such as by exposing time-based partitions or sampling data.
For example, a Batch Kwargs Generator could be configured to produce a SQL query that logically represents “rows in the Events table with a type code of ‘X’ that occurred within seven days of a given timestamp.” With that configuration, you could provide a timestamp as a partition name, and the Batch Kwargs Generator will produce instructions that a SQLAlchemyDatasource could use to materialize a SQLAlchemyDataset corresponding to that batch of data and ready for validation.
A batch is a sample from a data asset, sliced according to a particular rule. For example, an hourly slide of the Events table or “most recent users records.”
A Batch is the primary unit of validation in the Great Expectations DataContext. Batches include metadata that identifies how they were constructed–the same “batch_kwargs” assembled by the generator, “batch_markers” that provide more detailed metadata to aid in replicating complicated workflows, and optionally “batch_parameters” that include information such as an asset or partition name.
See more detailed documentation on the Generator Module.