Data pipelines don't manage themselves.
As workflows grow more complex, teams need a reliable way
to schedule tasks, handle failures automatically, and
monitor everything from a single place. Apache Airflow is
how the best engineering teams do exactly that — and this
book teaches you how to use it, from your very first DAG
all the way to a production deployment on AWS.
This is a practical, hands-on guide. Every chapter builds
on the last, every concept comes with real code, and by
the end you will have worked through a complete data
engineering workflow that mirrors what teams use in
production today.
WHAT YOU WILL LEARN
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Part 1 — Foundations
You will start by understanding what Airflow is, why it
exists, and how its core components fit together. Then
you will install it locally and write your first working
DAG — a real pipeline that runs on your own machine.
Part 2 — Building Pipelines
You will learn the tools data engineers use every day.
How to write Python tasks and pass data between them
using XComs. How to schedule pipelines and handle
historical backfills. How to store credentials securely
using Variables, Connections, and Secrets backends. How
to monitor DAG runs, read logs, and set up alerts.
Part 3 — Real-World Use Case
You will build a complete ETL pipeline — extracting data
from an API, transforming it with Pandas, creating the
target table, and loading it into PostgreSQL. You will
add production reliability with retry logic, SLA
monitoring, Slack alerts, and failure callbacks. Then
you will extend the pipeline to orchestrate AWS services
including S3, Lambda, and Redshift, and wire it into a
CI/CD workflow using Git and GitHub Actions.
Part 4 — Deployment and Scaling
You will move your pipelines to the cloud using AWS MWAA
— Amazon's fully managed Airflow service. You will set
up a complete MWAA environment from scratch, deploy your
DAGs via S3, and learn how to debug issues using
CloudWatch logs. You will also compare MWAA against
Google Cloud Composer and Astronomer so you can make an
informed choice for your own infrastructure.
Part 5 — Beyond the Basics
You will go deeper with advanced Airflow features —
building custom operators and plugins, generating tasks
dynamically at runtime using dynamic task mapping, and
optimising pipeline performance through scheduler tuning,
XCom management, and efficient operator design.
WHO THIS BOOK IS FOR
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This book is most useful if you are:
- A data engineer looking to adopt Airflow as your
orchestration layer
- A software developer moving into data infrastructure
- A data analyst or scientist whose scripts need to run
on a reliable schedule
- A DevOps or cloud engineer deploying Airflow in
production
You do not need prior Airflow experience. You will get
the most out of this book if you are comfortable with
Python, the command line, and basic SQL.
WHAT IS INCLUDED
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- 20 focused chapters across 5 parts
- Complete Airflow CLI command reference (Appendix A)
- Ready to use DAG templates — Simple ETL, Dynamic
Task Mapping, DAG Factory (Appendix B)
- Official documentation and community resource links
(Appendix C)
- 125 pages of practical, code-first content
TECHNICAL DETAILS
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- Written for Apache Airflow 2.x
- All code tested against Airflow 2.6+
- Python 3.8+
- AWS MWAA, S3, Lambda, Redshift examples included
