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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

Chapter 2: Implementing, Testing, and Deploying Basic Pipelines

Now that we are familiar with the basic concept of streaming data processing, in this chapter, we will take a deep dive into how to build something practical with Apache Beam.

The purpose of this chapter is to give you some hands-on experience of solving practical problems from start to finish. The chapter will be divided into subsections, with each following the same structure:

  1. Defining a practical problem
  2. Discussing the problem decomposition (and how to solve the problem using Beam's PTransform)
  3. Implementing a pipeline to solve the defined problem
  4. Testing and validating that we have implemented our pipeline correctly
  5. Deploying the pipeline, both locally and to a running cluster

During this process (mostly at Step 2), we will discuss the various possibilities provided by Beam for addressing the problem, and we will try to highlight any caveats or common issues you might run into...

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