Book Image

Microsoft Power BI Complete Reference

By : Devin Knight, Brian Knight, Mitchell Pearson, Manuel Quintana, Brett Powell
Book Image

Microsoft Power BI Complete Reference

By: Devin Knight, Brian Knight, Mitchell Pearson, Manuel Quintana, Brett Powell

Overview of this book

Microsoft Power BI Complete Reference Guide gets you started with business intelligence by showing you how to install the Power BI toolset, design effective data models, and build basic dashboards and visualizations that make your data come to life. In this Learning Path, you will learn to create powerful interactive reports by visualizing your data and learn visualization styles, tips and tricks to bring your data to life. You will be able to administer your organization's Power BI environment to create and share dashboards. You will also be able to streamline deployment by implementing security and regular data refreshes. Next, you will delve deeper into the nuances of Power BI and handling projects. You will get acquainted with planning a Power BI project, development, and distribution of content, and deployment. You will learn to connect and extract data from various sources to create robust datasets, reports, and dashboards. Additionally, you will learn how to format reports and apply custom visuals, animation and analytics to further refine your data. By the end of this Learning Path, you will learn to implement the various Power BI tools such as on-premises gateway together along with staging and securely distributing content via apps. This Learning Path includes content from the following Packt products: • Microsoft Power BI Quick Start Guide by Devin Knight et al. • Mastering Microsoft Power BI by Brett Powell
Table of Contents (25 chapters)
Title Page
About Packt
Contributors
Preface
Index

Dimension metrics


The majority of DAX measures will apply aggregating functions to numeric columns of fact tables. However, several of the most important metrics of a dataset are those which identify the presence of dimensions in fact tables such as the count of customers who've purchased and those who haven't. It can also be necessary to count the distinct values of a dimension column such as the number of postal codes sold to or the number of distinct marketing promotions over a period of time.

In the dataset for this project, the customer dimension table is exclusive to the Internet Sales fact table, and the measure should only count customers with internet sales history. Additionally, slowly changing dimension logic has been implemented so that a single customer defined by the Customer Alternate Key column could have multiple rows defined by the Customer Key column.

The following two DAX measures count the number of unique customers and products with internet sales history:

Internet Sales...