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Building Big Data Pipelines with Apache Beam

You're reading from   Building Big Data Pipelines with Apache Beam Use a single programming model for both batch and stream data processing

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Product type Paperback
Published in Jan 2022
Publisher Packt
ISBN-13 9781800564930
Length 342 pages
Edition 1st Edition
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Author (1):
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Jan Lukavský Jan Lukavský
Author Profile Icon Jan Lukavský
Jan Lukavský
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Table of Contents (13) Chapters Close

Preface 1. Section 1 Apache Beam: Essentials
2. Chapter 1: Introduction to Data Processing with Apache Beam FREE CHAPTER 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 15 – Implementing SchemaSportTracker

In this section, we will reimplement a task from Chapter 2, Implementing, Testing, and Deploying Basic Pipelines. We have included this to learn how to overcome some limitations of SQL when using schemas – notably, the (current) inability to perform aggregation (UDAF) using multiple fields. In our computation, we need to aggregate a composite (a Row) that has three fields – latitude, longitude, and timestamp.

Again, for clarity, let's recap the definition of our problem.

Problem definition

Given a stream of GPS locations and timestamps for a workout of a specific user (a workout has an ID that is guaranteed to be unique among all users), compute the performance metrics for each workout. These metrics should contain the total duration and distance elapsed from the start of the workout to the present.

Problem decomposition discussion

The actual business logic of computing the distance from GPS location...

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