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DATA PIPELINES WITH APACHE AIRFLOW, SECOND EDITION
JULIAN DE RUITER, ISMAEL CABRAL, KRIS GEUSEBROEK
Idioma: Inglés
Formatos: Pdf, ePub, MOBI, FB2
ISBN: 9781638357698
Editorial: Manning
Año de edición: 2026
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Overview
Simplify, streamline, and scale your data operations with data pipelines built on Apache Airflow.Apache Airflow provides a batteries-included platform for designing, implementing, and monitoring data pipelines. Building pipelines on Airflow eliminates the need for patchwork stacks and homegrown processes, adding security and consistency to the process. Now in its second edition, Data Pipelines with Apache Airflow teaches you to harness this powerful platform to simplify and automate your data pipelines, reduce operational overhead, and seamlessly integrate all the technologies in your stack. In Data Pipelines with Apache Airflow, Second Edition youll learn how to: • Master the core concepts of Airflow architecture and workflow design • Schedule data pipelines using the Dataset API and time tables, including complex irregular schedules • Develop custom Airflow components for your specific needs • Implement comprehensive testing strategies for your pipelines • Apply industry best practices for building and maintaining Airflow workflows • Deploy and operate Airflow in production environments • Orchestrate workflows in container-native environments • Build and deploy Machine Learning and Generative AI models using AirflowData Pipelines with Apache Airflow has empowered thousands of data engineers to build more successful data platforms. This new second edition has been fully revised to cover the latest features of Apache Airflow, including the Taskflow API, deferrable operators, and Large Language Model integration. Filled with real-world scenarios and examples, youll be carefully guided from Airflow novice to expert.About the bookData Pipelines with Apache Airflow, Second Edition teaches you how to build and maintain effective data pipelines. Youll master every aspect of directed acyclic graphs (DAGs)—the power behind Airflow—and learn to customize them for your pipelines specific needs. 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. Youll explore common Airflow usage patterns, including aggregating multiple data sources and connecting to data lakes, while discovering exciting new features such as dynamic scheduling, the Taskflow API, and Kubernetes deployments.About the reader For DevOps, data engineers, machine learning engineers, and sysadmins with intermediate Python skills.About the authorJulian de Ruiter is a Data + AI engineering lead at Xebia Data, with a background in computer and life sciences and a PhD in computational cancer biology. As consultant at Xebia Data, he enjoys helping clients design and build AI solutions and platforms, as well as the teams that drive them. From this work, he has extensive experience in deploying and applying Apache Airflow in production in diverse environments.Ismael Cabral is a Machine Learning Engineer and Airflow trainer with experience spanning across Europe, US, Mexico, and South America, where he has worked with market-leading companies. He has vast experience implementing data pipelines and deploying machine learning models in production.Kris Geusebroek is a data-engineering consultant with extensive hands-on experience with Apache Airflow ...