Apache Airflow in Action: Build Production-Ready Data Pipelines: Build, Schedule, and Scale Production-Ready Pipelines with ETL, PySpark, Databricks,
Modern data teams don't just move data - they orchestrate complex, scalable, and reliable pipelines. Apache Airflow has become the industry standard for workflow orchestration used by companies worldwide.
But most tutorials stop at basic DAG examples...
This book takes you all the way to real production mastery.
Whether you are an aspiring data engineer, ETL developer, or backend engineer moving into the modern data stack, this hands-on guide will teach you how to design, build, scale, and optimize Airflow pipelines used in real-world environments.
Inside This Book, You Will Learn How To:
Understand Airflow architecture from the ground up
Design clean and maintainable DAGs
Build end-to-end ETL pipelines
Orchestrate PySpark and Databricks workloads
Integrate Airflow with Kafka streaming pipelines
Implement retries, alerting, and failure handling
Scale Airflow using Celery and Kubernetes executors
Optimize scheduler and metadata database performance
Deploy Airflow on AWS, GCP, and Azure
Implement CI/CD for Airflow pipelines
Apply production best practices used by real data teams
Crack Apache Airflow interview questions with confidence
This Book Is Perfect For:
Aspiring data engineers
ETL developers
Spark and Databricks users
Backend engineers working with data pipelines
Professionals preparing for data engineering interviews
Anyone serious about mastering Apache Airflow
What Makes This Book Different
Unlike many theoretical guides, this book focuses on:
Real production scenarios
Hands-on pipeline design
Cloud and Spark integration
Performance tuning techniques
Interview-focused preparation
Step-by-step practical examples
You won't just learn Airflow - you'll learn how to use it like a professional data engineer.
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