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Learn Microsoft Fabric

You're reading from   Learn Microsoft Fabric A practical guide to performing data analytics in the era of artificial intelligence

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Product type Paperback
Published in Feb 2024
Publisher Packt
ISBN-13 9781835082287
Length 338 pages
Edition 1st Edition
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Authors (2):
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Bradley Schacht Bradley Schacht
Author Profile Icon Bradley Schacht
Bradley Schacht
Arshad Ali Arshad Ali
Author Profile Icon Arshad Ali
Arshad Ali
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Toc

Table of Contents (19) Chapters Close

Preface 1. Part 1: An Introduction to Microsoft Fabric FREE CHAPTER
2. Chapter 1: Overview of Microsoft Fabric and Understanding Its Different Concepts 3. Chapter 2: Understanding Different Workloads and Getting Started with Microsoft Fabric 4. Part 2: Building End-to-End Analytics Systems
5. Chapter 3: Building an End-to-End Analytics System – Lakehouse 6. Chapter 4: Building an End-to-End Analytics System – Data Warehouse 7. Chapter 5: Building an End-to-End Analytics System – Real-Time Analytics 8. Chapter 6: Building an End-to-End Analytics System – Data Science 9. Part 3: Administration and Monitoring
10. Chapter 7: Monitoring Overview and Monitoring Different Workloads 11. Chapter 8: Administering Fabric 12. Part 4: Security and Developer Experience
13. Chapter 9: Security and Governance Overview 14. Chapter 10: Continuous Integration and Continuous Deployment (CI/CD) 15. Part 5: AI Assistance with Copilot Integration
16. Chapter 11: Overview of AI Assistance and Copilot Integration 17. Index 18. Other Books You May Enjoy

Monitoring Real-Time Analytics activity

With Fabric Real-Time Analytics, there are two main areas that need to be monitored: eventstreams and KQL database activity. While there is no centralized report to see activity across all eventstreams and KQL databases inside a workspace, the relevant information can be found by navigating to each item individually. Because the experience is slightly different for each of these two items, let’s look at them individually.

Monitoring eventstreams

Recall from Chapter 5, Building an End-to-End Analytics System – Real-Time Analytics, that an eventstream captures, transforms, and routes events to destinations such as KQL databases or Fabric lakehouses. There are two options for monitoring the health and performance of an eventstream, and both are available from the eventstream editor.

The first option, Data insights, displays a variety of metrics related to performance and health. This chart allows you to easily determine if...

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