Summary

In this chapter, we covered the fundamentals of Apache Airflow – from installation to developing data pipelines. You learned how to leverage Airflow to orchestrate complex workflows involving data acquisition, processing, and integration with external systems.

We installed Airflow locally using the Astro CLI and Docker. This provided a quick way to get hands-on without a heavy setup. You were exposed to Airflow’s architecture and key components, such as the scheduler, worker, and metadata database. Understanding these pieces is crucial for monitoring and troubleshooting Airflow in production.

Then, there was a major section focused on building your first Airflow DAGs. You used core Airflow operators and the task and DAG decorators to define and chain tasks. We discussed best practices such as keeping tasks small and autonomous. You also learned how Airflow handles task dependencies – allowing parallel execution of independent tasks. These learnings...