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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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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

Defining splittable DoFn as a unification for bounded and unbounded sources

Beam offers a wide variety of source and sink transforms. We will not walk through them in this book because their details can be easily found online. In this book, we have used the KafkaIO transform heavily – other source and sink transforms are used analogously but specifically on the target storage system.

The question that arises is this: what should we do when there are either specific requirements for the way the data is read (or stored) or when we need to connect to a data source that Beam does not have a connector for? Let's first see how to implement a custom source.

A fundamental requirement for any source is that it has the ability to split itself. We need to split a bounded source in order to be able to parallelize its processing and we need to split an unbounded source to get a persistent moment in time we can return to in case of a failure. Such a moment in time is typically...

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