Data Pipelines with Apache Airflow, Second Edition

Data Pipelines with Apache Airflow has empowered thousands of data engineers to build more successful data platforms. This new second edition has been fully revised for Airflow 3 with coverage of all the latest features of Apache Airflow, including the Taskflow API, deferrable operators, and Large Language Model integration. Filled with real-world scenarios and examples, you'll be carefully guided from Airflow novice to expert.Using real-world scenarios and examples, this book teaches you how to simplify and automate data pipelines, reduce operational overhead, and smoothly integrate all the technologies in your stack. Part reference and part tutorial, each technique is illustrated with engaging hands-on examples, from training machine learning models for generative AI to optimizing delivery routes.In Data Pipelines with Apache Airflow, Second Edition you'll learn how to:Master the core concepts of Airflow architecture and workflow designSchedule data pipelines using the Dataset API and time tables, including complex irregular schedulesDevelop custom Airflow components for your specific needsImplement comprehensive testing strategies for your pipelinesApply industry best practices for building and maintaining Airflow workflowsDeploy and operate Airflow in production environmentsOrchestrate workflows in container-native environmentsBuild and deploy Machine Learning and Generative AI models using AirflowAbout the TechnologyApache Airflow provides a unified platform for collecting, consolidating, cleaning, and analyzing data. With its easy-to-use UI, powerful scheduling and monitoring features, plug-and-play options, and flexible Python scripting, Airflow makes it easy to implement secure, consistent pipelines for any data or AI task.About the bookData Pipelines with Apache Airflow, Second Edition teaches you how to build, monitor, and maintain effective data workflows. This new edition adds comprehensive coverage of Airflow 3 features, such as event-driven scheduling, dynamic task mapping, DAG versioning, and Airflows entirely new UI. The numerous examples address common use cases like data ingestion and transformation and connecting to multiple data sources, along with AI-aware techniques such as building RAG systems.What's insideDeploying data pipelines as Airflow DAGsTime and event-based scheduling strategiesIntegrating with databases, LLMs, and AI modelsDeploying Airflow using KubernetesAbout the readerFor data engineers, machine learning engineers, DevOps, and sysadmins with intermediate Python skills.About the authorJulian de Ruiter, Ismael Cabral, Kris Geusebroek, Daniel van der Ende, and Bas Harenslak are seasoned data engineers and Airflow experts.