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Microsoft Power BI Complete Reference

You're reading from   Microsoft Power BI Complete Reference Bring your data to life with the powerful features of Microsoft Power BI

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Product type Course
Published in Dec 2018
Publisher Packt
ISBN-13 9781789950045
Length 794 pages
Edition 1st Edition
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Authors (5):
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Brett Powell Brett Powell
Author Profile Icon Brett Powell
Brett Powell
Mitchell Pearson Mitchell Pearson
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Mitchell Pearson
Brian Knight Brian Knight
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Brian Knight
Devin Knight Devin Knight
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Devin Knight
Manuel Quintana Manuel Quintana
Author Profile Icon Manuel Quintana
Manuel Quintana
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Table of Contents (25) Chapters Close

Title Page
About Packt
Contributors
Preface
1. Getting Started with Importing Data Options FREE CHAPTER 2. Data Transformation Strategies 3. Building the Data Model 4. Leveraging DAX 5. Visualizing Data 6. Using a Cloud Deployment with the Power BI Service 7. Planning Power BI Projects 8. Connecting to Sources and Transforming Data with M 9. Designing Import and DirectQuery Data Models 10. Developing DAX Measures and Security Roles 11. Creating and Formatting Power BI Reports 12. Applying Custom Visuals, Animation, and Analytics 13. Designing Power BI Dashboards and Architectures 14. Managing Application Workspaces and Content 15. Managing the On-Premises Data Gateway 16. Deploying the Power BI Report Server 17. Creating Power BI Apps and Content Distribution 18. Administering Power BI for an Organization 19. Scaling with Premium and Analysis Services 1. Other Books You May Enjoy Index

Optimizing performance


One of the main reasons for creating a dataset, particularly an import mode dataset, is to provide a performant data source for reports and dashboards. Although Power BI supports traditional reporting workloads, such as email subscriptions and view-only usage, Power BI empowers users to explore and interact with reports and datasets. The responsiveness of visuals for this self-service workload is largely driven by fundamental data model design decisions, such as the granularity of fact and dimension tables. 

Additional performance factors outside the scope of this chapter include the hardware resources allocated to the dataset, such as with Power BI Premium capacities (v-cores, RAM), the efficiency of the DAX Measures created for the dataset, the design of the Power BI reports that query the dataset, and the volume and timing of queries generated by users. Beyond the DAX measures described in Chapter 10, Developing DAX Measures and Security Roles these other factors...

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