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Azure Data Engineer Associate Certification Guide

You're reading from   Azure Data Engineer Associate Certification Guide Ace the DP-203 exam with advanced data engineering skills

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Product type Paperback
Published in May 2024
Publisher Packt
ISBN-13 9781805124689
Length 548 pages
Edition 2nd Edition
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Authors (3):
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Newton Alex Newton Alex
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Newton Alex
Giacinto Palmieri Giacinto Palmieri
Author Profile Icon Giacinto Palmieri
Giacinto Palmieri
Mr. Surendra Mettapalli Mr. Surendra Mettapalli
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Mr. Surendra Mettapalli
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Table of Contents (17) Chapters Close

Preface 1. Part 1: Azure Basics FREE CHAPTER
2. Chapter 1: Introducing Azure Basics 3. Part 2: Data Storage
4. Chapter 2: Implementing a Partition Strategy 5. Chapter 3: Designing and Implementing the Data Exploration Layer 6. Part 3:Data Processing
7. Chapter 4: Ingesting and Transforming Data 8. Chapter 5: Developing a Batch Processing Solution 9. Chapter 6: Developing a Stream Processing Solution 10. Chapter 7: Managing Batches and Pipelines 11. Part 4:Secure, Monitor, and Optimize Data Storage and Processing
12. Chapter 8: Implementing Data Security 13. Chapter 9: Monitoring Data Storage and Data Processing 14. Chapter 10: Optimizing and Troubleshooting Data Storage and Data Processing 15. Chapter 11: Accessing the Online Practice Resources 16. Other Books You May Enjoy

Creating Windowed Aggregates

Windowed aggregates are essential for processing time series data, which is simply data recorded continuously over time. Examples of time series data could include stock prices recorded over time and IoT sensor values, which show the health of machinery over time.

Note

This section primarily focuses on the Create windowed aggregates concept of the DP-203: Data Engineering on Microsoft Azure exam.

In the subsequent sections, you’ll explore the different windowed aggregates that are available in ASA. ASA supports the following five types of windows:

  • Tumbling Windows: These are fixed-size, non-overlapping windows where each window covers a specific time duration, and data is aggregated separately for each window.
  • Hopping windows: Similar to Tumbling Windows, hopping windows also have a fixed size, but they can overlap with each other. This allows more flexibility in capturing data and analyzing trends over time.
  • Sliding windows...
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