You're reading from Microsoft Power BI Performance Best Practices Learn practical techniques for building high-speed Power BI solutions

Product type Paperback

Published in Aug 2024

Publisher Packt

ISBN-13 9781835082256

Length 346 pages

Edition 2nd Edition

Languages

DAX

Tools

Azure

Concepts

Business Intelligence

Authors (2):

Thomas LeBlanc

Bhavik Merchant

View More author details

Table of Contents (23) Chapters

Preface

1. Part 1: Architecture, Bottlenecks, and Performance Targets FREE CHAPTER

2. Chapter 1: Setting Targets and Identifying Problem Areas

3. Chapter 2: Exploring Power BI Architecture and Configuration

4. Chapter 3: Learning the Tools for Performance Tuning

5. Part 2: Performance Analysis, Improvement, and Management

6. Chapter 4: Analyzing Logs and Metrics

7. Chapter 5: Optimization for Storage Modes

8. Chapter 6: Third-Party Utilities

9. Chapter 7: Performance Governance Framework

10. Part 3: Fetching, Transforming, and Visualizing Data

11. Chapter 8: Loading, Transforming, and Refreshing Data

12. Chapter 9: Report and Dashboard Design

13. Part 4: Data Models, Calculations, and Large Semantic Models

14. Chapter 10: Dimensional Modeling and Row Level Security

15. Chapter 11: Improving DAX

16. Chapter 12: High Scale Patterns

17. Part 5: Optimizing Capacities in Power BI Enterprises

18. Chapter 13: Working with Capacities

19. Chapter 14: Performance Needs for Fabric Artifacts

20. Chapter 15: Embedding in Web Apps

21. Index

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Monitoring Fabric resource consumption

The main monitor help for the printing of this book is the Microsoft Fabric Capacity Metrics app. This is a Power BI template app you can install to monitor a Premium and/or Fabric capacity. The template app is explained in detail in Chapter 13. The details allow 30-second periods to be examined for all resources and the main measurement is CUs.

Figure 14.7 shows the list of SynapseNotebook resources used in a time slice of Fabric:

Figure 14.7 – Specific Fabric resource in monitor solution

Other resource types include Dataflow, Dataset, Lakehouse, and Warehouse. All these resources will show CU usage as well as durations for execution, queries, or data refreshes. The term “dataset” is referring to a semantic mode. The template app has not updated the header of this column yet.

To help you understand how to monitor resource consumption in real time, let’s look at a system function...