Search icon CANCEL
Subscription
0
Cart icon
Your Cart (0 item)
Close icon
You have no products in your basket yet
Arrow left icon
Explore Products
Best Sellers
New Releases
Books
Videos
Audiobooks
Learning Hub
Free Learning
Arrow right icon
Arrow up icon
GO TO TOP
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

Arrow left icon
Product type Paperback
Published in Jan 2022
Publisher Packt
ISBN-13 9781800564930
Length 342 pages
Edition 1st Edition
Languages
Concepts
Arrow right icon
Author (1):
Arrow left icon
Jan Lukavský Jan Lukavský
Author Profile Icon Jan Lukavský
Jan Lukavský
Arrow right icon
View More author details
Toc

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 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...

lock icon The rest of the chapter is locked
Register for a free Packt account to unlock a world of extra content!
A free Packt account unlocks extra newsletters, articles, discounted offers, and much more. Start advancing your knowledge today.
Unlock this book and the full library FREE for 7 days
Get unlimited access to 7000+ expert-authored eBooks and videos courses covering every tech area you can think of
Renews at $19.99/month. Cancel anytime
Banner background image