Book Image

Mastering Microsoft Power BI – Second Edition - Second Edition

By : Gregory Deckler, Brett Powell
4.5 (2)
Book Image

Mastering Microsoft Power BI – Second Edition - Second Edition

4.5 (2)
By: Gregory Deckler, Brett Powell

Overview of this book

Mastering Microsoft Power BI, Second Edition, provides an advanced understanding of Power BI to get the most out of your data and maximize business intelligence. This updated edition walks through each essential phase and component of Power BI, and explores the latest, most impactful Power BI features. Using best practices and working code examples, you will connect to data sources, shape and enhance source data, and develop analytical data models. You will also learn how to apply custom visuals, implement new DAX commands and paginated SSRS-style reports, manage application workspaces and metadata, and understand how content can be staged and securely distributed via Power BI apps. Furthermore, you will explore top report and interactive dashboard design practices using features such as bookmarks and the Power KPI visual, alongside the latest capabilities of Power BI mobile applications and self-service BI techniques. Additionally, important management and administration topics are covered, including application lifecycle management via Power BI pipelines, the on-premises data gateway, and Power BI Premium capacity. By the end of this Power BI book, you will be confident in creating sustainable and impactful charts, tables, reports, and dashboards with any kind of data using Microsoft Power BI.
Table of Contents (18 chapters)
16
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17
Index

Summary

This chapter built on the queries from Chapter 3, Connecting To Sources And Transforming Data With M, to implement import, DirectQuery, and composite analytical data models. Relationships were created between fact and dimension tables as well as between bridge tables and the Sales and Margin Plan to enable actual versus plan reporting and analysis.

Additionally, the fundamentals of designing Power BI models were reviewed and detailed guidance on metadata and the DMVs available for analyzing memory usage was provided. Finally, guidance was provided for optimizing the performance of import, DirectQuery, and composite data models.

The following chapter continues to build on the dataset for this project by developing analytical measures and security models. The DAX expressions implemented in the next chapter directly leverage the relationships defined in this chapter and ultimately drive the visualizations and user experience demonstrated in later chapters.

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