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Azure Data Engineer Associate Certification Guide

You're reading from   Azure Data Engineer Associate Certification Guide A hands-on reference guide to developing your data engineering skills and preparing for the DP-203 exam

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Product type Paperback
Published in Feb 2022
Publisher Packt
ISBN-13 9781801816069
Length 574 pages
Edition 1st Edition
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Author (1):
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Newton Alex Newton Alex
Author Profile Icon Newton Alex
Newton Alex
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Table of Contents (23) Chapters Close

Preface 1. Part 1: Azure Basics
2. Chapter 1: Introducing Azure Basics FREE CHAPTER 3. Part 2: Data Storage
4. Chapter 2: Designing a Data Storage Structure 5. Chapter 3: Designing a Partition Strategy 6. Chapter 4: Designing the Serving Layer 7. Chapter 5: Implementing Physical Data Storage Structures 8. Chapter 6: Implementing Logical Data Structures 9. Chapter 7: Implementing the Serving Layer 10. Part 3: Design and Develop Data Processing (25-30%)
11. Chapter 8: Ingesting and Transforming Data 12. Chapter 9: Designing and Developing a Batch Processing Solution 13. Chapter 10: Designing and Developing a Stream Processing Solution 14. Chapter 11: Managing Batches and Pipelines 15. Part 4: Design and Implement Data Security (10-15%)
16. Chapter 12: Designing Security for Data Policies and Standards 17. Part 5: Monitor and Optimize Data Storage and Data Processing (10-15%)
18. Chapter 13: Monitoring Data Storage and Data Processing 19. Chapter 14: Optimizing and Troubleshooting Data Storage and Data Processing 20. Part 6: Practice Exercises
21. Chapter 15: Sample Questions with Solutions 22. Other Books You May Enjoy

Implementing logging used by Azure Monitor

Azure Monitor is the service we use to monitor infrastructure, services, and applications. Azure Monitor records two types of data: metrics and logs. Metrics are numerical values that describe an entity or an aspect of a system at different instances of timeā€”for example, the number of gigabytes (GBs) of data stored in a storage account at any point in time, the current number of active pipelines in ADF, and so on. Metrics are stored in time-series databases and can be easily aggregated for alerting, reporting, and auditing purposes.

Logs, on the other hand, are usually text details of what is happening in the system. Unlike metrics, which are recorded at regular intervals, logs are usually event-driven. For example, a user logging in to a system, a web app receiving a REpresentational State Transfer (REST) request, and triggering a pipeline in ADF could all generate logs.

Since Azure Monitor is an independent service, it can aggregate...

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