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Apache Airflow for Data Engineering: Build Scalable ETL, ELT, and AI Pipelines with Python: A Complete Guide to Orchestrating Modern Data Workflows, A
Paperback
DatabasesGeneral ComputersProgramming
Publisher: Independently Published
Published: Dec 7 2025
Pages: 686
Weight: 1.99
Height: 1.37 Width: 6.00 Depth: 9.00
Language: English
Modern data systems live or die by their ability to move, transform, and operationalize information at scale. Apache Airflow for Data Engineering is the definitive guide to designing, orchestrating, and managing production-grade pipelines using Airflow 2.x, written by data engineering expert Takehiro Kanegi.
Across hundreds of organizations, Airflow has become the backbone of automated analytics, AI workflows, and enterprise ETL. This book teaches you not just how to use Airflow, but how to think like a workflow architect capable of building resilient, maintainable, and scalable systems.
You will learn the complete lifecycle of modern data pipelines, from ingestion and transformation to orchestration, monitoring, and optimization. Through real-world patterns and end-to-end project builds, you'll discover how to integrate Airflow with tools across the modern stack including Snowflake, BigQuery, Redshift, Spark, Kubernetes, object stores, APIs, and machine learning pipelines.
Whether you're building daily ETL, autonomous ELT models, or AI-driven production systems, this book gives you the blueprint, best practices, and architectural patterns required to deliver reliable automation at scale.
Inside, you'll learn how to:Build DAGs using Airflow's modern Pythonic features and best practices
Orchestrate large-scale ETL and ELT pipelines across cloud data platforms
Implement robust scheduling, dependency management, sensors, and triggers
Deploy Airflow using KubernetesExecutor, CeleryExecutor, Docker, or managed services
Integrate Airflow with Snowflake, BigQuery, Spark, S3, GCP, Azure, and REST/GraphQL APIs
Automate machine learning workflows for training, evaluation, and deployment
Engineer highly available Airflow environments with enterprise logging and observability
Apply production-ready patterns for retries, idempotency, SLAs, backfills, and lineage
Build fully automated data platforms that scale predictably with demand
Data engineers, ML engineers, analytics professionals, software engineers, and technical leaders who need to orchestrate reliable, automated workflows across complex data ecosystems. No prior Airflow experience required - only a foundation in Python.
Why This Book MattersAirflow is more than a scheduler. It is the operating system for data engineering and AI automation. Takehiro Kanegi delivers a comprehensive, deeply practical guide that shows you how to architect real systems, avoid common pitfalls, and build pipelines that work every time.
If you want your data workflows to be automated, scalable, and production-ready, this book will show you how to get there.
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