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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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Concepts
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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

Summary

In this chapter, we have walked through the last fundamental transform of Apache Beam – the splittable DoFn transform. The transform works as a unifying bridge between batch and streaming sources on one side and allows us to build reusable bounded and unbounded transforms that can be composed to deliver new functionality. As an example, we implemented a StreamingFileRead transform that composes two splittable DoFn transforms – one that watches a directory for new files and another that reads the contents of the files and produces PCollection objects of text lines from them. Note that we might reuse these transforms in different ways. The FileRead transform can be used to read filenames from Apache Kafka, thereby converting a stream in Kafka containing new filenames to a stream of text lines contained in these files. The DirectoryWatch transform could be used as an input to a transform that ensures the synchronizing of files between two distinct locations. It is...

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