Librería Samer Atenea
Kálamo Books
Librería Elías (Asturias)
Librería Kolima (Madrid)
Librería Proteo (Málaga)
Apache Airflow cemented its position as the de facto workflow automation and data orchestration tool; hence, mastering Airflow has become critical for ensuring data reliability and operational efficiency. As Airflow enters the version 3.0 era, shifting towards a decentralized, service-oriented architecture with a native focus on data assets, staying ahead of its design patterns is important for modern data teams.This book provides a comprehensive journey through the modern patterns of data orchestration. Readers will start with the origins and architecture of Airflow, followed by installation and configuration, core and special features, logging and monitoring, and popular managed versions. Each chapter delivers practical and hands-on insights that will help readers to practice side-by-side.By the end of this book, readers will possess the practical competencies required to design, develop, test, and deploy production-grade pipelines. Readers will also gain invaluable skills in the latest asset-centric development, micro-orchestration, and modern CI/CD practices, turning them into highly capable engineers ready to tackle complex, enterprise-scale data infrastructure challenges.WHAT YOU WILL LEARN● Learn Airflow components and core operational architectures.● Install and configure a local environment on your machine.● Design efficient DAGs using foundational operators.● Manage execution with XComs and hooks.● Implement flexible scheduling, triggering mechanisms, and robust monitoring.● Understand and relate how different providers are supporting managed instances.● New features in 3.0 and major shifts from the previous version.WHO THIS BOOK IS FORThe book is designed for data, analytics, and MLOps engineers alongside data scientists with basic Python, CLI, and database familiarity. It guides beginners from core concepts to intermediate-level production scaling, debugging, and automating cloud-based ETL and machine learning workflows.
