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Building Big Data Pipelines with Apache Beam

You're reading from Building Big Data Pipelines with Apache Beam Use a single programming model for both batch and stream data processing

Product type Paperback

Published in Jan 2022

Publisher Packt

ISBN-13 9781800564930

Length 342 pages

Edition 1st Edition

Languages

Python

Tools

Apache Beam

Concepts

Big Data

Author (1):

Jan Lukavský

View More author details

Table of Contents (13) Chapters

Preface

1. Section 1 Apache Beam: Essentials

2. Chapter 1: Introduction to Data Processing with Apache Beam FREE CHAPTER

3. Chapter 2: Implementing, Testing, and Deploying Basic Pipelines

4. Chapter 3: Implementing Pipelines Using Stateful Processing

5. Section 2 Apache Beam: Toward Improving Usability

6. Chapter 4: Structuring Code for Reusability

7. Chapter 5: Using SQL for Pipeline Implementation

8. Chapter 6: Using Your Preferred Language with Portability

9. Section 3 Apache Beam: Advanced Concepts

10. Chapter 7: Extending Apache Beam's I/O Connectors

11. Chapter 8: Understanding How Runners Execute Pipelines

12. Other Books You May Enjoy

Technical requirements

This chapter will return to the Java Software Development Kit (SDK), so the tools will be the same as those in Chapter 4, Structuring Code for Reusability. As always, we will need a cloned version of the GitHub repository for this book located at https://github.com/PacktPublishing/Building-Big-Data-Pipelines-with-Apache-Beam.

So, let's dive directly into the (admittedly non-trivial) concept of splittable DoFn!

The rest of the chapter is locked

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Authors (1)

Lukavský

Lukavský

Jan Lukavský is a freelance big data architect and engineer who is also a committer of Apache Beam. He is a certified Apache Hadoop professional. He is working on open source big data systems combining batch and streaming data pipelines in a unified model, enabling the rise of real-time, data-driven applications.

See other products by Lukavský

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