Fingerlin T. Practical Guide to Apache Airflow 3. 2025

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Fingerlin T. Practical Guide to Apache Airflow 3. 2025

Textbook in PDF format This report covers new features in Airflow 3 (including DAG versioning!), for new and experienced users alike, upgrading best practices and more. We’re excited to present the Practical Guide to Airflow 3, a comprehensive custom report developed in partnership with Manning. This resource provides an in-depth look at new capabilities in Airflow 3 with concrete ways to implement them and plenty of code examples. Whether you’re new to Airflow or an experienced user looking to upgrade your DAGs, this book will be your companion as you dive into Airflow 3. In this report, you’ll: Learn how to get started with Airflow 3, set up a local development environment and write your first pipeline (even if you’ve never used Airflow before). Explore new Airflow 3 features including DAG versioning, asset-oriented syntax and the new Airflow UI. Find detailed descriptions of important changes between Airflow 2 and 3 alongside instructions on how to prepare your DAGs for a smooth upgrading process. About the Authors Tamara is a Developer Advocate at Astronomer. She is passionate about helping data engineers build awesome pipelines with Apache Airflow, be it for bread-and-butter ETL use cases or next-generation MLOps applications. In her free time she likes to lucid dream, read and think about consciousness, and relax in VR. About Apache Airflow Apache Airflow is the open source standard for workflow orchestration, offering a flexible and scalable way to programmatically author, schedule, and monitor your data pipelines using Python and SQL. Originally created at Airbnb as an open-source project in 2014, Airflow was brought into the Apache Software Foundation’s Incubator Program in 2016 and announced as a Top-Level Apache Project in 2019. Now, it’s widely recognized as the industry’s leading data orchestration solution. With the 2025 launch of Apache Airflow 3, new major features offer great improvements to ease of use, security and the ability to run workflows anywhere at any time for building modern data pipelines at scale