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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

Implementing our first streaming pipeline using SQL

We will follow the same path that we walked when we started playing with the Java SDK of Apache Beam. The very first pipeline we implemented, which was in Chapter 1, Introducing Data Processing with Apache Beam, was a pipeline that read input from a resource file named lorem.txt. Our goal was to process this file and output the number of occurrences of words within that file. So, let's see how our solution would differ if we used SQL to solve it!

We have implemented the equivalent of com.packtpub.beam.chapter1.FirstPipeline in com.packtpub.beam.chapter5.FirstSQLPipeline. The main differences are summarized here:

  1. First, we need to create a Schema that will represent our input. The input is raw lines of text as String objects, so a possible Schema representing it is a single-field Schema defined as follows:
    Schema lineSchema = Schema.of(
        Field.of("s", FieldType.STRING));
  2. We then attach...
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