Search icon CANCEL
Subscription
0
Cart icon
Your Cart (0 item)
Close icon
You have no products in your basket yet
Save more on your purchases now! discount-offer-chevron-icon
Savings automatically calculated. No voucher code required.
Arrow left icon
Explore Products
Best Sellers
New Releases
Books
Videos
Audiobooks
Learning Hub
Conferences
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

Chapter 6: Using Your Preferred Language with Portability

In the previous chapters, we focused on the Java SDK – or various Java SDK-based DSLs – but what if we want to implement our data transformation logic in a completely different language, such as Python or Go? One of the main goals of Apache Beam is portability. We have already seen the portability of pipelines between different Runners and between batch and streaming semantics. In this chapter, we will explore the last aspect of portability – portability between SDKs.

We will outline how the portability layer works (Apache Beam often calls it the Fn API – pronounced Fun API) so that the result is portable. The desired goal is to enable Runners so that they don't have to understand the SDK (the language we want to use to implement our pipeline), yet can still execute it successfully. That way, new SDKs can be created without us needing to make modifications to the currently existing Runners...

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