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Building Big Data Pipelines with Apache Beam

You're reading from  Building Big Data Pipelines with Apache Beam

Product type Book
Published in Jan 2022
Publisher Packt
ISBN-13 9781800564930
Pages 342 pages
Edition 1st Edition
Languages
Author (1):
Jan Lukavský Jan Lukavský
Profile icon Jan Lukavský

Table of Contents (13) Chapters

Preface Section 1 Apache Beam: Essentials
Chapter 1: Introduction to Data Processing with Apache Beam Chapter 2: Implementing, Testing, and Deploying Basic Pipelines Chapter 3: Implementing Pipelines Using Stateful Processing Section 2 Apache Beam: Toward Improving Usability
Chapter 4: Structuring Code for Reusability Chapter 5: Using SQL for Pipeline Implementation Chapter 6: Using Your Preferred Language with Portability Section 3 Apache Beam: Advanced Concepts
Chapter 7: Extending Apache Beam's I/O Connectors Chapter 8: Understanding How Runners Execute Pipelines 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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