Data Pipelines with Apache Airflow
- 10h 17m 57s
- Bas Harenslak, Julian de Ruiter
- Manning Publications
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.
- 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