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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

Understanding schemas

A schema is a way of describing a nested structure of a Java object. A typical Java object contains fields that have string names and data types.

Let's see the following Java class:

public class Position {
  double latitude; 
  double longitude;
}

This class has two fields called latitude and longitude, respectively, both of which are of the double type. The matching Schema property of this class would be as follows:

Position: Row
  latitude: double
  longitude: double

This notation declared a Position type with a schema of the Row type containing two fields, latitude and longitude, both of the double type. A Row is one of Apache Beam's built-in schema types with a nested structure – the others are Array, Iterable, and Map with their usual definitions in computer science. The difference between Array and Iterable is that Iterable does not have a known size until it's iterated over. This...

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