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keyboard_arrow_down In-Depth Examples & Production Best Practices
Data Engineering Path  ·  Airflow
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Hands-On Quiz — Production Practices

Practice Exercises

Exercise 1: Fix the Anti-Patterns

Take the "bad" DAG from Module 1's quiz and refactor it into a production-ready pipeline with proper error handling, retry logic, idempotency, and monitoring.

Exercise 2: Build a Monitoring DAG

Create a "meta-DAG" that monitors the health of your other DAGs:

  • Check if any DAG Runs are stuck in "running" for more than 2 hours
  • Send a Slack alert for any failed DAG Runs
  • Generate a daily health report

Exercise 3: CI/CD Pipeline

Set up a GitHub Actions workflow that:

  1. Runs pytest on all DAG files (DagBag validation)
  2. Checks for Python syntax errors
  3. Deploys DAGs to an S3 bucket on merge to main
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