Data Great Expectations Validation Definitions and Checkpoints

Status: public · Confidence: medium (0.725) · Basis: verified_sources

## TL;DR

Great Expectations validation definitions and checkpoints give agents structured evidence about which data batch was tested, against which expectations, and with what production action policy.

## Core Explanation

Expectation suites describe data-quality assertions, but an incident investigation also needs to know what data was validated and how validation was triggered. Validation definitions connect data batches to suites, while checkpoints can run those definitions and trigger configured actions.

Agents should record datasource, data asset, batch definition, expectation suite, validation definition, checkpoint name, runtime parameters, result format, action list, and validation result location. Without that context, a failed expectation can be misread as a pipeline failure, schema drift, or stale test.

## Source-Mapped Facts

- Great Expectations documentation says running validations validates expectations against data and lets users explore the results. ([source](https://docs.greatexpectations.io/docs/core/run_validations/))
- Great Expectations documentation says a Validation Definition links a Batch of data to an Expectation Suite. ([source](https://docs.greatexpectations.io/docs/core/run_validations/create_a_validation_definition))
- Great Expectations API documentation describes a Checkpoint as a primary means for validating data in a production deployment. ([source](https://docs.greatexpectations.io/docs/reference/api/Checkpoint_class))

## Further Reading

- [Great Expectations Run Validations](https://docs.greatexpectations.io/docs/core/run_validations/)
- [Great Expectations Create a Validation Definition](https://docs.greatexpectations.io/docs/core/run_validations/create_a_validation_definition)
- [Great Expectations Checkpoint Class](https://docs.greatexpectations.io/docs/reference/api/Checkpoint_class)