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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 5 – Calculating performance statistics for a sport activity tracking application

Let's explore the most useful applications of stream processing – the delivery of high-accuracy real-time insights to (possibly) high-volume data streams. As an example, we will borrow a use case known to almost everyone – calculating performance statistics (for example, speed and total distance) from a stream of GPS coordinates coming from a sport activity tracker!

Defining the problem

Given an input data stream of quadruples (workoutId, gpsLatitude, gpsLongitude, and timestamp) calculate the current speed and the total tracked distance of the tracker. The data comes from a GPS tracker that sends data only when its user starts a sport activity. We can assume that workoutId is unique and contains a userId value in it.

Let's describe the problem more informally. Suppose we have a stream that looks as follows:

(user1:track1, 65.5384, -19.9108, 1616427100000...
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