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Mastering Microsoft Power BI

Mastering Microsoft Power BI - Second Edition

By : Greg Deckler, Brett Powell
4.6 (56)
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Mastering Microsoft Power BI

Mastering Microsoft Power BI

4.6 (56)
By: Greg 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)
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16
Other Books You May Enjoy
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Index

SQL views

As described in the Dataset planning section of Chapter 1, Planning Power BI Projects, a set of SQL views should be created within the data source and these SQL views, rather than the database tables, should be accessed by the Power BI dataset. SQL views are essentially virtual tables that provide an abstraction layer from the underlying database tables. SQL views can be used to merge database tables and to limit the number of columns, thus preventing such transformations from occurring within Power Query queries.

Each fact and dimension table required by the Power BI dataset should have its own SQL view and its own M query within the dataset that references this view. The SQL views should preferably be assigned to a dedicated database schema and identify the dimension or fact table represented as shown in Figure 2.11:

Figure 2.11: Views assigned to BI schema in SQL Server

A common practice is to create a database schema specific to the given dataset being...

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