Introduction to Apache Airflow in Python

You build DAGs, schedule and automate pipelines, and monitor their runs, all in Python. The problem Airflow solves is one every growing data team hits: a pile of scripts held together by cron jobs and hope, with no clear view of what ran, what failed, or what depends on what. Airflow replaces that with defined dependencies, automatic retries and visibility, which is why it became the default orchestrator across the industry.

It assumes you build pipelines and know Python, so it's not a first data course. It sits on DataCamp's subscription (roughly $14/month on the annual plan, first chapter free), and it leaves a completion certificate that marks what you covered, not a qualification. Airflow is a widely adopted, durable orchestration tool (as of 2026).

Introduction to Apache Airflow in Python teaches workflow orchestration: building DAGs (Directed Acyclic Graphs), scheduling and automating data pipelines, monitoring and debugging runs, and — in the final chapter — combining triggers, branching logic, and human-approval gates into a complete production-style pipeline. It's kept current, explicitly updated to Apache Airflow 3.1.6.