May 2021
Beginner to intermediate
480 pages
12h 59m
English
Overview
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
About the Reader
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.
Quotes
An Airflow bible. Useful for all kinds of users, from novice to expert.
- Rambabu Posa, Sai Aashika Consultancy
An easy-to-follow exploration of the benefits of orchestrating your data pipeline jobs with Airflow.
- Daniel Lamblin, Coupang
The one reference you need to create, author, schedule, and monitor workflows with Apache Airflow. Clear recommendation.
- Thorsten Weber, bbv Software Services AG
By far the best resource for Airflow.
- Jonathan Wood, LexisNexis
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.
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.
Quotes
An Airflow bible. Useful for all kinds of users, from novice to expert.
- Rambabu Posa, Sai Aashika Consultancy
An easy-to-follow exploration of the benefits of orchestrating your data pipeline jobs with Airflow.
- Daniel Lamblin, Coupang
The one reference you need to create, author, schedule, and monitor workflows with Apache Airflow. Clear recommendation.
- Thorsten Weber, bbv Software Services AG
By far the best resource for Airflow.
- Jonathan Wood, LexisNexis
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