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

Summary

A successful pipeline moves data efficiently, minimizing pauses and blockages between tasks, keeping every process 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, Data Pipelines with Apache Airflow 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. Its easy-to-use UI, plug-and-play options, and flexible Python scripting make Airflow perfect for any data management task.

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 reader

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

Table of Contents

PART 1 - GETTING STARTED

1 Meet Apache Airflow

2 Anatomy of an Airflow DAG

3 Scheduling in Airflow

4 Templating tasks using the Airflow context

5 Defining dependencies between tasks

PART 2 - BEYOND THE BASICS

6 Triggering workflows

7 Communicating with external systems

8 Building custom components

9 Testing

10 Running tasks in containers

PART 3 - AIRFLOW IN PRACTICE

11 Best practices

12 Operating Airflow in production

13 Securing Airflow

14 Project: Finding the fastest way to get around NYC

PART 4 - IN THE CLOUDS

15 Airflow in the clouds

16 Airflow on AWS

17 Airflow on Azure

18 Airflow in GCP

About the Authors

Bas Harenslak and Julian de Ruiter are data engineers with extensive experience using Airflow to develop pipelines for major companies including Heineken, Unilever, and Booking.com. Bas is a committer, and both Bas and Julian are active contributors to Apache Airflow.