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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 Section 1 Apache Beam: Essentials
Chapter 1: Introduction to Data Processing with Apache Beam Chapter 2: Implementing, Testing, and Deploying Basic Pipelines Chapter 3: Implementing Pipelines Using Stateful Processing Section 2 Apache Beam: Toward Improving Usability
Chapter 4: Structuring Code for Reusability Chapter 5: Using SQL for Pipeline Implementation Chapter 6: Using Your Preferred Language with Portability Section 3 Apache Beam: Advanced Concepts
Chapter 7: Extending Apache Beam's I/O Connectors Chapter 8: Understanding How Runners Execute Pipelines Other Books You May Enjoy

Exploring the Apache Beam capability matrix

Beam makes sure that runners adhere to the Beam model to ensure that pipelines are portable between different runners. There are tests specifically designed to validate the compatibility between runners – these tests are called ValidatesRunner suites and are annotated using the @ValidatesRunner annotation. Any runner author can verify the compatibility of their runner against these tests. However, because runners are developed at different rates, it is possible that a specific runner at a certain moment in time does not support all of the features of the Beam model. Therefore, the complete set of ValidatesRunner tests can be narrowed down by toggling specific features of the model. The tests in the complete suite are then annotated using features such as UsesSetState or UsesSchema. A runner is then allowed to specify features that should be excluded from the tests. These excluded features should then match what is documented in the...

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