Data Pipelines with Apache Airflow

  • 10h 17m 57s
  • Bas Harenslak, Julian de Ruiter
  • Manning Publications
  • 2021

Data Pipelines with Apache Airflow teaches you how to build and maintain effective data pipelines.


A successful pipeline moves data efficiently, minimizing pauses and blockages between tasks, and keeping processes along the way operational. Apache Airflow provides a single customizable environment for building and managing data pipelines, eliminating the need for a hodgepodge collection of tools, snowflake code, and homegrown processes. Using real-world scenarios and examples, this book teaches you how to simplify and automate data pipelines, reduce operational overhead, and smoothly integrate all the technologies in your stack.

About the Technology

Data pipelines manage the flow of data from initial collection through consolidation, cleaning, analysis, visualization, and more. Apache Airflow provides a single platform you can use to design, implement, monitor, and maintain your pipelines. It features easy-to-use UI, plug-and-play options, and flexible Python scripting.

About the Book

Data Pipelines with Apache Airflow teaches you how to build and maintain effective data pipelines. You’ll explore the most common usage patterns, including aggregating multiple data sources, connecting to and from data lakes, and cloud deployment. Part reference and part tutorial, this practical guide covers every aspect of the directed acyclic graphs (DAGs) that power Airflow, and how to customize them for your pipeline’s needs.

What's Inside

  • Build, test, and deploy Airflow pipelines as DAGs
  • Automate moving and transforming data
  • Analyze historical datasets using backfilling
  • Develop custom components
  • Set up Airflow in production environments

About the Audience

For DevOps, data engineers, machine learning engineers, and sysadmins with intermediate Python skills.

About the Authors

Bas Harenslak and Julian de Ruiter are data engineers with extensive experience using Airflow to develop pipelines for major companies. Bas is also an Airflow committer.

In this Audiobook

  • Chapter 1 - Meet Apache Airflow
  • Chapter 2 - Anatomy of an Airflow DAG
  • Chapter 3 - Scheduling in Airflow
  • Chapter 4 - Templating tasks using the Airflow context
  • Chapter 5 - Defining dependencies between tasks
  • Chapter 6 - Triggering workflows
  • Chapter 7 - Communicating with external systems
  • Chapter 8 - Building custom components
  • Chapter 9 - Testing
  • Chapter 10 - Running tasks in containers
  • Chapter 11 - Best practices
  • Chapter 12 - Operating Airflow in production
  • Chapter 13 - Securing Airflow
  • Chapter 14 - Finding the fastest way to get around NYC
  • Chapter 15 - Airflow in the clouds
  • Chapter 16 - Airflow on AWS
  • Chapter 17 - Airflow on Azure
  • Chapter 18 - Airflow in GCP