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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

Summary

In this chapter, we investigated various ways to effectively structure our code to enable better reusability. We learned how to write our own PTransform and how the PTransform expansion works. We saw different types of objects serving as PInput – input objects to PTransform – or POutput – the output objects of PTransform. We looked at the most common examples of these objects – PCollection, PCollectionList, and PCollectionTuple. We also looked at two special cases – PBegin and PDone – which serve as the root and leaf nodes in the computational DAG, respectively. We also learned about the CoGroupByKey composite transform, which can be used to perform windowed joins.

Then, we explored a DSL that offers a wrapper around CoGroupByKey – the Join library. This library offers all types of windowed joins – inner joins, one-sided outer joins, and full outer joins. We used this library to create an extension of our SportTracker...

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