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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 portability layer

In this section, we will walk through the design of the portability layer – the FnAPI – to understand which components are orchestrated together to allow pipelines to be executed from different SDKs on the same Runner.

First, let's see how the whole portability layer works. This concept is illustrated in the following (somewhat simplified) diagram:

Figure 6.1 – The portability layer architecture

As we can see, the architecture consists of two types of components – Apache Beam components and Runner components. In this case, a Runner is a piece of technology that performs the actual execution – it may be Apache Flink, Apache Spark, Google Cloud Dataflow, or any other supported Runner. Each of these Runners typically has a coordinator that needs to receive a job submission and use this submission to create work for worker nodes. By doing this, it can orchestrate its execution. This coordinator...

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