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Building Big Data Pipelines with Apache Beam

You're reading from  Building Big Data Pipelines with Apache Beam

Product type Book
Published in Jan 2022
Publisher Packt
ISBN-13 9781800564930
Pages 342 pages
Edition 1st Edition
Languages
Author (1):
Jan Lukavský Jan Lukavský
Profile icon Jan Lukavský

Table of Contents (13) Chapters

Preface 1. Section 1 Apache Beam: Essentials
2. Chapter 1: Introduction to Data Processing with Apache Beam 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 4: Structuring Code for Reusability

We have already walked through a great deal of the Apache Beam programming model, but we haven't investigated one of its core primitives – PTransform. We have seen many particular instances of PTransforms, but what if we wanted to implement our own? And should we even do that in the first place? In this chapter, we will explain how exactly Apache Beam builds the Directed Acyclic Graph (DAG) of operations, and we will use this knowledge to build a Domain Specific Language (DSL) to solve a specific use case that uses less boilerplate code than just by using plain Apache Beam. Then, we will introduce some of the built-in DSLs of Apache Beam. Last, but not least, we will learn how to view a stream of data as a time-varying relation, which is a fancy term for a table changing in time, which will help us establish a base to introduce one additional DSL – SQL. That will be the topic of Chapter 5, Using SQL for Pipeline Implementation...

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