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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

Using side outputs

As the name suggests, side inputs are something that is added to the main input from the side, while side outputs are something that is output from the DoFn object outside of the main PCollection output. Let's start with the side outputs, as they are more straightforward.

As an example, let's imagine we are processing data coming in as JSON values. We need to parse these messages into an internal object. But what should we do with the values that cannot be parsed because they contain a syntax error? If we do not do any validation before we store them in the stream (topic), then it is certainly possible that we will encounter such a situation. We can silently drop those records, but that is obviously not a great idea, as that could cause hard-to-debug problems. A much better option would be to store these values on the side to be able to investigate and fix them. Therefore, we should aim to do the following:

Figure 3.8 – Main...

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