Most Airflow DAGs have zero data quality checks. The pipeline runs, data lands in the warehouse, and you find out something is wrong when a stakeholder asks why the dashboard numbers look off. Three days later.

Adding quality checks feels like a project: pick a tool, configure it, write checks for every table, maintain them as schemas change. So it never happens.

Here's how to add auto-generated data quality checks to any Airflow DAG in under 5 minutes. No configuration, no writing checks by hand.

Option 1: BashOperator (zero install beyond pip)

If you already have DQLens installed in your Airflow environment:

from airflow import DAG from airflow.operators.bash import BashOperator from datetime import datetime

with DAG ( " my_pipeline " , start_date = datetime ( 2026 , 1 , 1 ), schedule = " @daily " ) as dag :

load_data = BashOperator ( task_id = " load_data " , bash_command = " python load_script.py " , )

quality_check = BashOperator ( task_id = " quality_check " , bash_command = ( " dqlens init…