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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
Toc

Table of Contents (13) Chapters close

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

Summary

In this chapter, we learned about all the remaining primitive transforms. We now know the details of both the stateless and stateful ParDo objects. We know the basic life cycle of DoFn and understand the concept of bundles. We understand why input to stateful ParDo objects has to be in the form of keyed PCollection objects. We have seen and understood the details of how states and timers are managed by Beam and how they are delegated to runners in order to ensure fault tolerance. We know how a watermark propagates in transforms in general and what the (stateful) transform's input watermark and output watermark are. We have successfully used our knowledge to create our version of the GroupIntoBatches transform, which stores data into states before delegating them to an external RPC service.

Next, we focused on handling late and droppable data to be able to avoid data loss. We created one simple and one sophisticated version of a transform process to filter (split) data...

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