How to run a Checkpoint in Airflow

This guide will help you run a Great Expectations checkpoint in Apache Airflow, which allows you to trigger validation of a data asset using an Expectation Suite directly within an Airflow DAG.

Using checkpoints is the most straightforward way to trigger a validation run from within Airflow. The following sections describe two alternative approaches to accomplishing this.

Running a checkpoint with a BashOperator

You can use a simple BashOperator in Airflow to trigger the checkpoint run. The following snippet shows an Airflow task for an Airflow DAG named dag that triggers the run of a checkpoint we named my_checkpoint:

validation_task = BashOperator(
    task_id='validation_task',
    bash_command='great_expectations checkpoint run my_checkpoint',
    dag=dag
)

Running the checkpoint script output with a PythonOperator

Another option is to use the output of the checkpoint script command and paste it into a method that is called from a PythonOperator in the DAG. This gives you more fine-grained control over how to respond to validation results:

  1. Run checkpoint script

    great_expectations checkpoint script my_checkpoint
    
    ...
    
    A python script was created that runs the checkpoint named: `my_checkpoint`
    - The script is located in `great_expectations/uncommitted/my_checkpoint.py`
    - The script can be run with `python great_expectations/uncommitted/my_checkpoint.py`
    
  2. Navigate to the generated Python script and copy the content

  3. Create a method in your Airflow DAG file and call it from a PythonOperator:

    def run_checkpoint():
        # paste content from the checkpoint script here
    
    task_run_checkpoint = PythonOperator(
        task_id='run_checkpoint',
        python_callable=run_checkpoint,
        dag=dag,
    )