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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

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Product type Paperback
Published in Jan 2022
Publisher Packt
ISBN-13 9781800564930
Length 342 pages
Edition 1st Edition
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Author (1):
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Jan Lukavský Jan Lukavský
Author Profile Icon Jan Lukavský
Jan Lukavský
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Table of Contents (13) Chapters Close

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

The legacy Source API and the Read transform

Before the creation of the splittable DoFn object, Beam used the Source API and its associated Read transform. Although this transform is currently deprecated and should not be used for implementing new sources, it is still supported. On some runners and under specific conditions, using the deprecated Read transform might still be preferred. We have already seen examples of this – for example, the use_deprecated_read flag passed when using the --experiments flag for Python's ReadFromKafka transform.

The Read transform accepts a single parameter: either an object of the BoundedSource type or the UnboundedSource type. Whether the source is bounded or unbounded then determines if the resulting PCollection object is bounded or unbounded.

We apply the Read transform as follows:

Pipeline p = ...;
p.apply(Read.from(new MyUnboundedSource());

We will not go into the details of BoundedSource or UnboundedSource, mostly because...

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