Data Pipelines with Apache Airflow
eBook - ePub

Data Pipelines with Apache Airflow

  1. 480 pages
  2. English
  3. ePUB (mobile friendly)
  4. Available on iOS & Android
eBook - ePub

Data Pipelines with Apache Airflow

About this book

"An Airflow bible. Useful for all kinds of users, from novice to expert." - Rambabu Posa, Sai Aashika Consultancy

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, 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.

Purchase of the print book includes a free eBook in PDF, Kindle, and ePub formats from Manning Publications.

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 author
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.

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

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Yes, you can access Data Pipelines with Apache Airflow by Julian de Ruiter,Bas Harenslak in PDF and/or ePUB format, as well as other popular books in Computer Science & Cloud Computing. We have over one million books available in our catalogue for you to explore.

Information

Table of contents

  1. inside front cover
  2. Data Pipelines with Apache Airflow
  3. Copyright
  4. brief contents
  5. contents
  6. front matter
  7. Part 1. Getting started
  8. 1 Meet Apache Airflow
  9. 2 Anatomy of an Airflow DAG
  10. 3 Scheduling in Airflow
  11. 4 Templating tasks using the Airflow context
  12. 5 Defining dependencies between tasks
  13. Part 2. Beyond the basics
  14. 6 Triggering workflows
  15. 7 Communicating with external systems
  16. 8 Building custom components
  17. 9 Testing
  18. 10 Running tasks in containers
  19. Part 3. Airflow in practice
  20. 11 Best practices
  21. 12 Operating Airflow in production
  22. 13 Securing Airflow
  23. 14 Project: Finding the fastest way to get around NYC
  24. Part 4. In the clouds
  25. 15 Airflow in the clouds
  26. 16 Airflow on AWS
  27. 17 Airflow on Azure
  28. 18 Airflow in GCP
  29. appendix A. Running code samples
  30. appendix B. Package structures Airflow 1 and 2
  31. appendix C. Prometheus metric mapping
  32. index
  33. inside back cover