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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 17 – Implementing MaxWordLength in the Python SDK

We will use the well-known examples, which have mostly been implemented using the Java SDK, from Chapter 2, Implementing, Testing, and Deploying Basic Pipelines, and Chapter 3, Implementing Pipelines Using Stateful Processing. We will also build on our knowledge from Chapter 4, Structuring Code for Reusability, regarding using user-defined PTransforms for better reusability and testing.

Our first complete task will be the task we implemented as Task 2 in Chapter 2, Implementing, Testing, and Deploying Basic Pipelines, but as always, for clarity, we will restate the problem here.

Problem definition

Given an input data stream of lines of text, calculate the longest word found in this stream. Start with an empty word; once a longer word is seen, output the newly found candidate.

Problem decomposition discussion

From a logical perspective, this problem is the same as in the case of Task 2. So, let's focus...

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