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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

Introducing the primitive PTransform object – Combine

So far, we have seen three grouping (stateful) transformations: Count, Top, and Max. None of these are actually primitive transformations. A primitive transformation is defined as a transformation that needs direct support from a runner and cannot be executed via other transformations. The Combine object is actually the first primitive PTransform object that we are going to introduce. Beam actually has only five primitive PTransform objects, and we will walk through all of them in this chapter. We call non-primitive PTransform objects composite transformations.

The Combine PTransform object generally performs a reduction operation on a PCollection object. As the name suggests, the transform combines multiple input elements into a single output value per window (Combine.globally) or per key and window (Combine.perKey). This reduction is illustrated by the following figure:

Figure 2.6 –...

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