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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
Toc

Table of Contents (13) Chapters close

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

Task 14 – Implementing SQLMaxWordLength

In Chapter 2, Implementing, Testing, and Deploying Basic Pipelines, we implemented a pipeline called MaxWordLength. In this task, we will reimplement it by using SQL and schemas. Note that although we already know how to structure code better and use PTransforms rather than using static methods to transform one PCollection into another, we will keep the approach from the original chapter so that we can easily spot the differences and compare both versions more easily.

For clarity, let's restate the problem.

Problem definition

Given a stream of text lines in Apache Kafka, create a stream consisting of the longest word seen in the stream from the beginning to the present. Use triggering to output the result as frequently as possible. Use Apache Beam SQL to implement the task whenever possible.

Problem decomposition discussion

The interesting parts will be centered around several problems that we need to solve:

    ...
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