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How to create a new Checkpoint

This guide will help you create a new Checkpoint, which allows you to couple an Expectation Suite with a data set to validate.

Note: As of Great Expectations version 0.13.7, we have updated and improved the Checkpoints feature. You can continue to use your existing legacy Checkpoint workflows if you’re working with concepts from the Batch Kwargs (v2) API. If you’re using concepts from the BatchRequest (v3) API, please refer to the new Checkpoints guides.

Steps for Checkpoints (>=0.13.12)#

1. First, run the CLI command below.#
great_expectations --v3-api checkpoint new my_checkpoint
2. Next, you will be presented with a Jupyter Notebook which will guide you through the steps of creating a checkpoint.#

Additional Notes#

Within this notebook, you will have the opportunity to create your own yaml Checkpoint configuration. The following text walks through an example.

SimpleCheckpoint Example#

2a. Here is a simple example configuration.#

For this example, we’ll demonstrate using a basic Checkpoint configuration with the SimpleCheckpoint class, which takes care of some defaults. Replace all names such as my_datasource with the respective DataSource, DataConnector, DataAsset, and Expectation Suite names you have configured in your great_expectations.yml.

config = """name: my_checkpointconfig_version: 1class_name: SimpleCheckpointvalidations:    - batch_request:datasource_name: my_datasourcedata_connector_name: my_data_connectordata_asset_name: MyDataAssetdata_connector_query:    index: -1expectation_suite_name: my_suite"""

This is the minimum required to configure a Checkpoint that will run the Expectation Suite my_suite against the data asset MyDataAsset. See How to configure a new Checkpoint using test_yaml_config for advanced configuration options.

2b. Test your config using context.test_yaml_config.#
context.test_yaml_config(yaml_config=config)

When executed, test_yaml_config will instantiate the component and run through a self_check procedure to verify that the component works as expected.

In the case of a Checkpoint, this means

  1. validating the yaml configuration,
  2. verifying that the Checkpoint class with the given configuration, if valid, can be instantiated, and
  3. printing warnings in case certain parts of the configuration, while valid, may be incomplete and need to be better specified for a successful Checkpoint operation.

The output will look something like this:

Attempting to instantiate class from config...Instantiating as a SimpleCheckpoint, since class_name is SimpleCheckpointSuccessfully instantiated SimpleCheckpoint

Checkpoint class name: SimpleCheckpoint

If something about your configuration wasn’t set up correctly, test_yaml_config will raise an error.

2c. Store your Checkpoint config.#

After you are satisfied with your configuration, save it by running the appropriate cells in the Jupyter Notebook.

2d. (Optional:) Check your stored Checkpoint config.#

If the Store Backend of your Checkpoint Store is on the local filesystem, you can navigate to the checkpoints store directory that is configured in great_expectations.yml and find the configuration files corresponding to the Checkpoints you created.

2e. (Optional:) Test run the new Checkpoint and open Data Docs.#

Now that you have stored your Checkpoint configuration to the Store backend configured for the Checkpoint Configuration store of your Data Context, you can also test context.run_checkpoint, right within your Jupyter Notebook by running the appropriate cells.

Before running a Checkpoint, make sure that all classes and Expectation Suites referred to in the configuration exist.

When run_checkpoint returns, the checkpoint_run_result can then be checked for the value of the success field (all validations passed) and other information associated with running the specified actions.

For more advanced configurations of Checkpoints, please see How to configure a new Checkpoint using test_yaml_config.

Additional Resources#