great_expectations.cli.checkpoint

Module Contents

Functions

checkpoint()

Checkpoint operations

checkpoint_new(checkpoint, suite, directory, datasource)

Create a new checkpoint for easy deployments. (Experimental)

_verify_checkpoint_does_not_exist(context: DataContext, checkpoint: str, usage_event: str)

_write_checkpoint_to_disk(context: DataContext, checkpoint: dict, checkpoint_name: str)

_load_checkpoint_yml_template()

checkpoint_list(directory)

List configured checkpoints. (Experimental)

checkpoint_run(checkpoint, directory)

Run a checkpoint. (Experimental)

checkpoint_script(checkpoint, directory)

Create a python script to run a checkpoint. (Experimental)

_validate_at_least_one_suite_is_listed(context: DataContext, batch: dict, checkpoint_file: str)

_load_script_template()

_write_checkpoint_script_to_disk(context_directory: str, checkpoint_name: str, script_path: str)

great_expectations.cli.checkpoint.SQLAlchemyError
great_expectations.cli.checkpoint.SQLAlchemyError
great_expectations.cli.checkpoint.yaml
great_expectations.cli.checkpoint.checkpoint()

Checkpoint operations

A checkpoint is a bundle of one or more batches of data with one or more Expectation Suites.

A checkpoint can be as simple as one batch of data paired with one Expectation Suite.

A checkpoint can be as complex as many batches of data across different datasources paired with one or more Expectation Suites each.

Feature Maturity

icon-1363e1e0ed6f11eaa1210242ac110002 Checkpoint - Notebook - How-to Guide
Run a configured checkpoint from a notebook.
Maturity: Experimental
Details:
API Stability: Unstable (expect changes to batch definition; “assets to validate” is still totally untyped)
Implementation Completeness: Complete
Unit Test Coverage: Partial (“golden path”-focused tests; error checking tests need to be improved)
Integration Infrastructure/Test Coverage: N/A
Documentation Completeness: Complete
Bug Risk: Low
icon-1363e38eed6f11eaa1210242ac110002 Checkpoint - Command Line - How-to Guide
Run a configured checkpoint from a command line in a Terminal shell.
Maturity: Experimental
Details:
API Stability: Unstable (expect changes to batch definition; no checkpoint store)
Implementation Completeness: Complete
Unit Test Coverage: Complete
Integration Infrastructure/Test Coverage: N/A
Documentation Completeness: Complete
Bug Risk: Low
icon-1363e474ed6f11eaa1210242ac110002 Checkpoint - Cron - How-to Guide
Use the Unix crontab command to edit the cron file and add a line that will run checkpoint as a scheduled task.
Maturity: Experimental
Details:
API Stability: Unstable (expect changes to batch validation; no checkpoint store)
Implementation Completeness: Complete
Unit Test Coverage: Complete
Integration Infrastructure/Test Coverage: N/A
Documentation Completeness: Complete
Bug Risk: Low
icon-1363e532ed6f11eaa1210242ac110002 Checkpoint - Airflow DAG - How-to Guide
Running a configured checkpoint in Apache Airflow enables the triggering of data validation using an Expectation Suite directly within an Airflow DAG.
Maturity: Beta
Details:
API Stability: Unstable
Implementation Completeness: Partial (no operator, but probably don’t need one)
Unit Test Coverage: N/A
Integration Infrastructure/Test Coverage: Minimal
Documentation Completeness: Complete (pending how-to)
Bug Risk: Low
icon-1363e5f0ed6f11eaa1210242ac110002 Checkpoint - Kedro - How-to Guide
TODO: Checkpoint - Kedro Description
Maturity: Experimental
Details:
API Stability: Unknown (implemented by Kedro team)
Implementation Completeness: Unknown
Unit Test Coverage: Unknown
Integration Infrastructure/Test Coverage: Unknown
Documentation Completeness: Minimal (none)
Bug Risk: Unknown
icon-1363e6aeed6f11eaa1210242ac110002 Checkpoint - Prefect - How-to Guide
TODO: Checkpoint - Prefect Description
Maturity: Experimental
Details:
API Stability: Unknown (implemented by Prefect team)
Implementation Completeness: Unknown
Unit Test Coverage: Unknown
Integration Infrastructure/Test Coverage: Unknown
Documentation Completeness: Minimal (none)
Bug Risk: Unknown
icon-1363e762ed6f11eaa1210242ac110002 Checkpoint - DBT - How-to Guide
TODO: Checkpoint - DBT Description
Maturity: Beta
Details:
API Stability: Mostly Stable (SQLAlchemy)
Implementation Completeness: Minimal
Unit Test Coverage: Minimal (none)
Integration Infrastructure/Test Coverage: Minimal (none)
Documentation Completeness: Minimal (none)
Bug Risk: Low
great_expectations.cli.checkpoint.checkpoint_new(checkpoint, suite, directory, datasource)

Create a new checkpoint for easy deployments. (Experimental)

great_expectations.cli.checkpoint._verify_checkpoint_does_not_exist(context: DataContext, checkpoint: str, usage_event: str) → None
great_expectations.cli.checkpoint._write_checkpoint_to_disk(context: DataContext, checkpoint: dict, checkpoint_name: str) → str
great_expectations.cli.checkpoint._load_checkpoint_yml_template() → dict
great_expectations.cli.checkpoint.checkpoint_list(directory)

List configured checkpoints. (Experimental)

great_expectations.cli.checkpoint.checkpoint_run(checkpoint, directory)

Run a checkpoint. (Experimental)

great_expectations.cli.checkpoint.checkpoint_script(checkpoint, directory)

Create a python script to run a checkpoint. (Experimental)

Checkpoints can be run directly without this script using the great_expectations checkpoint run command.

This script is provided for those who wish to run checkpoints via python.

great_expectations.cli.checkpoint._validate_at_least_one_suite_is_listed(context: DataContext, batch: dict, checkpoint_file: str) → None
great_expectations.cli.checkpoint._load_script_template() → str
great_expectations.cli.checkpoint._write_checkpoint_script_to_disk(context_directory: str, checkpoint_name: str, script_path: str) → None