Connect GX Cloud and Airflow

In this quickstart, you'll learn how to use GX Cloud with Apache Airflow. You'll create a simple DAG that runs a Checkpoint that you have already set up in GX Cloud, and then trigger it through a local installation of an Airflow server.

Apache Airflow is an orchestration tool that allows you to schedule and monitor your data pipelines. For more information about Apache Airflow, see the Apache Airflow documentation.

Prerequisites

• You have a GX Cloud account.

• You have installed Apache Airflow and initialized the database (airflow db init).

• You have connected GX Cloud to a Data Asset on a Data Source.

• You have created an Expectation Suite and added Expectations.

• You have added a Checkpoint to your Expectation.

Create a local Airflow project and set dependencies

1. Open a terminal, navigate to the directory where you want to create your Airflow project, and then run the following code:

   Terminal input

   mkdir gx-cloud-airflow && cd gx-cloud-airflow

   After running the CLI, a new directory is created and you're taken to that directory.

2. Start the Airflow Scheduler and Web Server

   airflow scheduler
   airflow webserver

   The scheduler manages task scheduling and the web server starts the UI for Airflow.

3. Access Airflow UI:

   Once the web server is running, open a web browser and go to http://localhost:8080 (by default) to access the Airflow UI.

Create a DAG file for your GX Cloud Checkpoint

1. Open a terminal, browse to the dags folder of your Airflow project, and then run the following code to create a new DAG named gx_dag.py:

   Terminal input

   touch gx_dag.py

2. Open the gx_dag.py DAG file and add the following code:

   Python

   import os
   import great_expectations as gx
   from airflow import DAG
   from airflow.operators.python import PythonOperator
   from datetime import datetime
   
   # Replace <YOUR_ACCESS_TOKEN> and <YOUR_CLOUD_ORGANIZATION_ID> with your credentials.
   
   GX_CLOUD_ACCESS_TOKEN = "<YOUR_ACCESS_TOKEN>"
   GX_CLOUD_ORGANIZATION_ID = "<YOUR_CLOUD_ORGANIZATION_ID>"
   
   CHECKPOINT_NAME = "<YOUR_CHECKPOINT>"
   
   def run_gx_airflow():
   
       context = gx.get_context()
       checkpoint = context.get_checkpoint(name = CHECKPOINT_NAME)
       checkpoint.run()
   
   default_args = {
       'owner': 'airflow',
       'depends_on_past': False,
       'start_date': datetime(2023, 8, 9),
   }
   
   gx_dag = DAG(
       'gx_dag',
       default_args=default_args,
       schedule_interval= '0 0 * * *', # This is set to run daily at midnight. Adjust as needed.
       catchup=False
   )
   
   run_gx_task = PythonOperator(
       task_id='gx_airflow',
       python_callable=run_gx_airflow,
       dag=gx_dag,
   )
   
   run_gx_task

3. Save your changes and close the gx_dag.py DAG file.

Run the DAG (Manually)

1. Sign in to Airflow. The default username and password are admin.

2. In the Actions column, click Trigger DAG for gx_airflow and confirm your DAG runs as expected.