Deploying Great Expectations with Airflow

This guide will help you deploy Great Expectations within an Airflow pipeline. You can use Great Expectations to automate validation of data integrity and navigate your DAG based on the output of validations.

Prerequisites: This workflow pattern assumes you have already:

There are two supported methods: using an Airflow PythonOperator to run Validations using python code or invoking the Great Expectations CLI to run a Checkpoint using an Airflow BashOperator.


There is not currently a Great Expectations custom Airflow operator, however we have heard from users who have implemented their own. We love community contributions so please share your operators!

Check out this link for an example of Airflow pipelines with Great Expectations Validations ensuring that downstream tasks are protected from upstream issues:

In the first link and the diagram below, you can see a common pattern of using Validation tasks to ensure that the data flowing from one task to the next is correct, and alert the team if it is not. Another common pattern is to branch and change your DAG based on a Validation (e.g. send data for more cleaning before moving to the next task, store it for a postmortem, etc.).

Airflow pipeline from Great Expectations tutorials repository.

We will now explain the two supported methods for using Great Expectations within an Airflow pipeline.

Running a Validation using a PythonOperator

  1. Create Validation Methods

    Create the methods to validate data that will be called in your DAG. In this example our data is contained in a file.

from airflow import AirflowException
from airflow.operators.python_operator import PythonOperator
import great_expectations as ge


def validate_data(ds, **kwargs):

    # Retrieve your data context
    context = ge.data_context.DataContext(<insert path to your great_expectations.yml>)

    # Create your batch_kwargs
    batch_kwargs_file = {
        "path": <insert path to your data file>,
        "datasource": "my_pandas_datasource"}

    # Create your batch (batch_kwargs + expectation suite)
    batch_file = context.get_batch(batch_kwargs_file, <insert name of your expectation suite>)

    # Run the validation
    results = context.run_validation_operator(
        # This run_id can be whatever you choose
        run_id=f"airflow: {kwargs['dag_run'].run_id}:{kwargs['dag_run'].start_date}")

    # Handle result of validation
    if not results["success"]:
        raise AirflowException("Validation of the data is not successful ")
  1. Add Validation Methods to DAG

    Validation steps can be added after data retrieval, transformation or loading steps to ensure that the steps were completed successfully.

# Create validation task
task_validate_data = PythonOperator(

# Add to DAG

Running a Validation using a Checkpoint & BashOperator

Please see this how-to guide for How to run a Checkpoint in Airflow.

Additional resources