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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

Explaining PTransform expansion

A PTransform is a short name for parallel transform – an Apache Beam primitive for transforming PInput into POutput. PInput is a labeling interface that marks objects as suitable as input to PTransform, while POutput marks objects as suitable as outputs. We already know these objects quite well – a typical one that's used for both input and output is PCollection. But there are others as well – most notably PCollectionTuple and PCollectionList. There are also two special objects – PBegin and PDone. As we already know, an Apache Beam program – a pipeline – is a DAG whose edges represent PCollections and whose nodes represent PTransforms. PTransforms in the DAG that take PBegin as input are roots, while PTransforms that produce PDone are the leaves of the DAG.

This can be seen in the following diagram:

Figure 4.1 – DAG of PTransforms and PCollections

A PTransform is a recursive...

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