Apache Airflow
Apache Airflow is an open-source platform for programmatically authoring, scheduling, and monitoring data pipelines. Workflows are defined as Python code using Directed Acyclic Graphs (DAGs), which makes them dynamic, testable, and version-controlled. Originally created at Airbnb in 2014, it became a top-level Apache Software Foundation project in 2019 and is now the de facto standard for workflow orchestration in the data engineering ecosystem.
In 2026, virtually every modern data stack — from ETL pipelines feeding data warehouses to MLOps workflows that train and deploy models — requires a reliable orchestrator, and Airflow is the most widely adopted choice. AI companies hire for it because ML pipelines (data ingestion, feature engineering, model retraining, evaluation) map naturally onto DAG-based scheduling. Its deep integration with cloud providers (AWS, GCP, Azure) and tools like Spark, dbt, and Kubernetes makes it a core skill for data and ML engineers building production systems.
🎓 Courses
The Complete Hands-On Introduction to Apache Airflow 3
by Marc Lamberti
The most widely recommended Airflow course on Udemy, rated 4.6 from nearly 14,000 reviews. Marc Lamberti leads customer education at Astronomer (the commercial Airflow platform) so the content reflects real production usage. Covers DAGs, operators, executors, plugins, XComs, and the new Airflow 3 asset syntax.
Apache Airflow: The Hands-On Guide
by Marc Lamberti
A deeper companion course from the same instructor focused on advanced production topics: scaling with Celery and Kubernetes executors, high-availability setups, integrations with AWS and GCP, and CI/CD patterns for DAG deployments.
Apache Airflow | A Real-Time & Hands-On Course on Airflow
by Bigdata Engineer
Rated 4.6 with 19,000+ students and updated in 2025. Goes deep on XComs, Hooks, Pools, SubDAGs, Variables, Connections, Plugins, Branching, Sensors, and Trigger rules with real-world hands-on examples.
Introduction to Apache Airflow
by Astronomer team
Free, authoritative beginner guide from the team that builds the most widely used commercial Airflow distribution. Covers core concepts, common use cases, architecture components, and pointers to deeper learning — a solid starting point before any paid course.
Getting Started with Apache Airflow
by DataCamp team
A structured interactive tutorial covering Airflow installation, configuration, and writing your first DAG. Good complement to video courses for learners who prefer reading and doing.
📖 Books
Apache Airflow Best Practices
Dylan Intorf, Dylan Storey, Kendrick van Doorn · 2024
Published October 2024 by Packt and available on O'Reilly. Focuses on production deployment, cloud environments (AWS and GCP), custom plugins, monitoring, and scaling for high availability — the practical knowledge gaps that beginner courses leave unfilled.
Data Pipelines with Apache Airflow, Second Edition
Bas P. Harenslak, Julian de Ruiter · 2025
Manning MEAP released December 2024, full publication estimated August 2025. Updates the widely-used first edition for Airflow 3, covering the Dataset API, timetables, custom operators, and industry best practices. The canonical book-length reference for the current version.
Practical Guide to Apache Airflow 3
Astronomer and Manning authors · 2025
Free ebook published in 2025 alongside the Airflow 3 launch by Astronomer in partnership with Manning. Covers the major Airflow 3 changes: DAG versioning, asset-oriented syntax, the new UI, and migration from Airflow 2. Ideal for engineers already familiar with Airflow 2 who need to upgrade.
🛠️ Tutorials & Guides
Apache Airflow Quick Start — Official Documentation
The authoritative first step. Sets up a local Airflow instance and walks through core concepts directly from the source. Always reflects the current stable version.
Introduction to Apache Airflow
Well-structured written tutorial covering DAGs, the TaskFlow API, Dynamic Task Mapping, deferrable tasks, and the Triggerer. Good bridge between conceptual understanding and writing real code.
A Complete Apache Airflow Tutorial: Building Data Pipelines with Python
End-to-end tutorial focused on building a real data pipeline with Python. Covers environment setup through scheduling and monitoring, with an ML-adjacent framing that is useful for data scientists moving into pipeline engineering.
🏅 Certifications
Astronomer Certification for Apache Airflow Fundamentals
Astronomer · Free (exam-based)
The only widely recognized Airflow-specific certification, issued by Astronomer, the main commercial backer of Airflow. Tests DAG authoring, scheduling, operators, and core architecture. Recognized by hiring teams at data-heavy companies as a credible signal of hands-on competence.
Learning resources last updated: June 18, 2026