Apache Airflow

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