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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

Section 3 Apache Beam: Advanced Concepts

In this section, you will cover more advanced concepts and see how splittable DoFn closes the gap between batch and streaming I/Os. It also covers how to use splittable DoFn for non-I/O applications. The section ends with a detailed explanation of how a typical Apache Beam runner evaluates pipelines, and what are the typical building blocks of such a runner.

This section comprises the following chapters:

Chapter 7, Extending Apache Beam's I/O Connectors
Chapter 8, Understanding How Runners Execute Pipelines

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.

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