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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 7: Extending Apache Beam's I/O Connectors

In previous chapters, we focused on how to write data transformations after reading the data from data sources. There are two types of sources: bounded and unbounded. The difference between these is obvious – the size of the bounded type is limited (and this limitation is known in advance), while the size of the unbounded type is (possibly) infinite. A classic example of a bounded source is a file (or a set of immutable files), while an unbounded source is typically a streaming source such as Apache Kafka. Note that we can always convert an unbounded source to a bounded one by defining a bounding constraint. This could be, for example, the number of records that we want to read or the (processing or event time) duration for which we want to read the data.

In Apache Beam, these two types of sources historically resulted in two types of interfaces that are currently considered deprecated: the BoundedSource and UnboundedSource...

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