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.
Prerequisites: This how-to guide assumes you have already:
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:
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`
Navigate to the generated Python script and copy the content
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, )