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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 looked at the general design of Apache Beam's portability layer. We understood how this layer is designed so that both Runners and various SDKs can be developed independently so that once a portable Runner is implemented, it should be capable of running any SDK, even if the SDK did not exist at the time the Runner was implemented.

We then had a deep dive into the Python SDK, which builds heavily on the portability layer. We saw that the core Apache Beam model concepts are mirrored by all SDKs. Not all SDKs have the same set of features at the moment, but the set of supported features should converge over time.

We reimplemented some of our well-known examples from the Java SDK into the Python SDK to learn how to write and submit pipelines to a portable Runner – we used FlinkRunner for this, and we will continue to do so for the rest of this book. Next, we explored interactive programming using InteractiveRunner and Python notebooks. We saw...

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