Airflow
Apache Airflow is an open-source platform for programmatically authoring, scheduling, and monitoring data pipelines using Python code. Workflows are defined as Directed Acyclic Graphs (DAGs), where each node is a task and edges define dependencies. Airflow provides a web UI, a rich operator ecosystem, and support for scaling executors from local to Celery and Kubernetes.
Airflow has become the de-facto standard for orchestrating data and ML pipelines at scale, and it underpins the ETL, feature-engineering, and model-retraining workflows that AI products rely on. Airflow 3, released in 2025, introduced event-driven scheduling, DAG versioning, and LLM integration hooks, making it directly relevant to modern AI engineering roles. Teams hiring for MLOps, data engineering, and AI platform positions list Airflow proficiency among their most-requested skills.
🎓 Courses
The Complete Hands-On Introduction to Apache Airflow 3
by Marc Lamberti
The most widely recommended beginner entry-point for Airflow, updated for Airflow 3 in December 2025. Covers DAGs, Sensors, Hooks, Taskflow API, and XCOMs through practical examples.
Apache Airflow: The Hands-On Guide
by Marc Lamberti
Builds a full Forex Data Pipeline project and covers scaling Airflow with Local, Celery, and Kubernetes executors. Rated 4.4 from over 5,500 reviews and last updated November 2025.
Apache Airflow Best Practices
by Packt
Updated December 2025. Focuses on production deployment patterns, cloud environments (AWS and GCP), and extending Airflow with custom plugins. Good complement to intro courses.
Tutorials — Airflow 3 Official Documentation
by Apache Airflow community
The canonical starting point. Free, always up to date with the latest stable release, and covers core concepts including DAG authoring, scheduling, and the TaskFlow API with runnable examples.
Introduction to Apache Airflow
by Astronomer
Astronomer is the primary commercial backer of Airflow. Their free learn portal offers deep-dive guides on every Airflow concept, written by core contributors and kept current with Airflow 3.
📖 Books
Data Pipelines with Apache Airflow, Second Edition
Julian de Ruiter, Ismael Cabral, Kris Geusebroek, Daniel van der Ende, Bas Harenslak · 2025
Fully revised for Airflow 3 with coverage of event-driven scheduling, dynamic task mapping, DAG versioning, the new UI, and LLM/RAG integration. The definitive book on Airflow from Manning, written by five practitioners including an Apache Airflow committer.
Apache Airflow Best Practices
Dylan Intorf, Dylan Storey, Kendrick van Doorn · 2024
Published October 2024 by Packt (available on O'Reilly). Targets intermediate-to-advanced users who need to deploy Airflow in cloud environments and extend it with custom plugins and advanced configuration patterns.
🛠️ Tutorials & Guides
A Practical Guide to Modern Airflow
Concise, code-focused walkthrough of modern Airflow patterns including the TaskFlow API and dynamic task mapping. Good bridge between official docs and production use.
Apache Airflow Explained: A Beginner-Friendly Guide
Clear conceptual explanation of DAGs, Operators, Schedulers, and Executors with diagrams. Ideal for readers new to workflow orchestration who need mental models before writing code.
Introduction to Apache Airflow
Step-by-step tutorial that walks through installing Airflow, writing a first DAG, and understanding scheduling. Well-structured for self-paced learners coming from a data science background.
🏅 Certifications
Astronomer Certification for Apache Airflow Fundamentals
Astronomer · Free
The most recognized Airflow-specific certification, offered by the primary commercial steward of the project. Tests core DAG authoring, scheduling, and operator knowledge. Well-regarded by hiring teams in data engineering.
Learning resources last updated: June 18, 2026