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Version: 0.16.16

How to create a new Checkpoint

This guide will help you create a new CheckpointThe primary means for validating data in a production deployment of Great Expectations., which allows you to couple an Expectation SuiteA collection of verifiable assertions about data. with a data set to ValidateThe act of applying an Expectation Suite to a Batch..

Prerequisites​

Steps​

1. Create a Checkpoint​

In this guide, we will use the SimpleCheckpoint class, which takes care of some defaults.

To modify this code sample for your use case, replace the batch_request and expectation_suite_name with your own.

checkpoint = gx.checkpoint.SimpleCheckpoint(
    name="my_checkpoint",
    data_context=context,
    validations=[
        {
            "batch_request": batch_request,
            "expectation_suite_name": "my_expectation_suite",
        },
    ],
)

Note: There are other configuration options for more advanced deployments, please refer to How to configure a new Checkpoint using test_yaml_config for more details.

2. (Optional) Run your Checkpoint​

checkpoint_result = checkpoint.run()

The returned checkpoint_result contains information about the checkpoint run.

3. (Optional) Build Data Docs​

You can build Data DocsHuman readable documentation generated from Great Expectations metadata detailing Expectations, Validation Results, etc. with the latest checkpoint run result included by running:

context.build_data_docs()

4. (Optional) Store your Checkpoint​

If you want to store your Checkpoint for later use:

context.add_checkpoint(checkpoint=checkpoint)

And retrieve via the name we set earlier:

retrieved_checkpoint = context.get_checkpoint(name="my_checkpoint")

Additional Resources​