Validate pipeline data with Great Expectations

Steps

1. Install Great Expectations: pip install great_expectations; initialize a project with great_expectations init
2. Connect a data source using the Python API or CLI, pointing to a pandas DataFrame, SQL table, or file source to get a Validator object
3. Build an Expectation Suite by calling expectation methods on the Validator: validator.expect_column_values_to_not_be_null('user_id'); validator.expect_column_values_to_be_between('age', min_value=0, max_value=120); save the suite with validator.save_expectation_suite()
4. Create a Checkpoint that bundles a Batch Request with an Expectation Suite: define the checkpoint in Python or YAML referencing the data asset and suite name
5. Run the Checkpoint: checkpoint_result = checkpoint.run() — this validates the data and produces Validation Results
6. Inspect results programmatically: checkpoint_result['success'] returns True if all expectations passed; integrate into a pipeline by raising an exception on failure