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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 11 – Enhancing SportTracker by runner motivation using side inputs

In our first task for this chapter, we will enhance the SportTracker application we used in Task 5. We want to create motivating push notifications for users who are currently on track. Users will be notified every minute with information on whether their running performance over the last minute was better than their average pace over the last 5 minutes. Let's look at this problem in more detail.

Problem definition

Calculate two per-user running averages over the stream of artificial GPS coordinates that we generated for Task 5. One computation will be the average pace over a longer (5-minute) interval, while the other will be over a shorter (1-minute) interval. Every minute, for each user, output information will be provided regarding whether the user's current 1-minute pace is higher or lower than the longer average if the short average differs by more than 10%.

We will use our output_topic...

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