Book Image

Microsoft Power BI Cookbook

By : Author Test, Brett Powell

Book Image

Microsoft Power BI Cookbook

By: Author Test, Brett Powell

Overview of this book

Microsoft Power BI is a business intelligence and analytics platform consisting of applications and services designed to provide coherent, visual and interactive insights of data. This book will provide thorough, technical examples of using all primary Power BI tools and features as well as demonstrate high impact end-to-end solutions that leverage and integrate these technologies and services. Get familiar with Power BI development tools and services, go deep into the data connectivity and transformation, modeling, visualization and analytical capabilities of Power BI, and see Power BI’s functional programming languages of DAX and M come alive to deliver powerful solutions to address common, challenging scenarios in business intelligence. This book will excite and empower you to get more out of Power BI via detailed recipes, advanced design and development tips, and guidance on enhancing existing Power BI projects.

Preface

What this book covers

What you need for this book

Who this book is for

Reader feedback

Customer support

Free Chapter

Configuring Power BI Development Tools

Configuring Power BI Development Tools

Configuring Power BI Desktop options and settings

Installing the On-Premises Data Gateway

Installing Power BI Publisher for Excel

Installing and Configuring DAX Studio

Accessing and Retrieving Data

Accessing and Retrieving Data

Viewing and analyzing M functions

Establishing and managing connections to data sources

Building source queries for DirectQuery models

Importing data to Power BI Desktop models

Applying multiple filtering conditions

Choosing columns and column names

Transforming and cleansing source data

Creating custom and conditional columns

Integrating multiple queries

Choosing column data types

Visualizing the M library

Building a Power BI Data Model

Building a Power BI Data Model

Designing a multi fact data model

Implementing a multi fact data model

Handling one-to-many and many-to-many relationships

Assigning data formatting and categories

Configuring Default Summarization and sorting

Setting the visibility of columns and tables

Embedding business definitions into DAX measures

Enriching a model with analysis expressions

Building analytics into data models with DAX

Integrating math and statistical analysis via DAX

Supporting virtual table relationships

Creating browsable model hierarchies and groups

Authoring Power BI Reports

Authoring Power BI Reports

Building rich and intuitive Power BI reports

Creating table and matrix visuals

Utilizing graphical visualization types

Enhancing exploration of reports

Integrating card visualizations

Controlling interactive filtering between visuals

Associating slicers with report pages

Applying filters at different scopes

Formatting reports for publication

Designing mobile report layouts

Creating Power BI Dashboards

Creating Power BI Dashboards

Building a Power BI dashboard

Constructing an enterprise dashboard

Developing dynamic dashboard metrics

Preparing datasets and reports for Q & A natural language queries

Embedding analytical context into visualizations

Exposing what matters - top N and percentage of total visualizations

Visualizing performance relative to targets with KPIs and gauges

Leveraging Power BI reports in Power BI dashboards

Deploying content from Excel and SSRS to Power BI

Adding data alerts and email notifications to dashboards

Getting Serious with Date Intelligence

Getting Serious with Date Intelligence

Building a complete date dimension table

Prepping the date dimension via the Query Editor

Authoring date intelligence metrics across granularities

Developing advanced date intelligence metrics

Simplifying date intelligence with DAX queries and calculated tables

Adding a metric placeholder dimension

Parameterizing Power BI Solutions

Parameterizing Power BI Solutions

Creating dynamic and portable Power BI reports

Filtering queries with parameters

Preserving report metadata with Power BI templates

Converting static queries into dynamic functions

Parameterizing your data sources

Generating a list of parameter values via queries

Capturing user selections with parameter tables

Building a forecasting process with What if analysis capabilities

Implementing Dynamic User-Based Visibility in Power BI

Implementing Dynamic User-Based Visibility in Power BI

Capturing the current user context of Power BI content

Defining RLS roles and filtering expressions

Designing dynamic security models in Power BI

Building dynamic security in DirectQuery data models

Displaying the current filter context in Power BI reports

Avoiding manual user clicks with user-based filtering logic

Applying Advanced Analytics and Custom Visuals

Applying Advanced Analytics and Custom Visuals

Incorporating advanced analytics into Power BI reports

Enriching Power BI content with custom visuals and quick insights

Creating geospatial mapping visualizations with ArcGIS maps for Power BI

Configuring custom KPI and slicer visuals

Building animation and story telling capabilities

Embedding statistical analyses into your model

Creating and managing Power BI groupings and bins

Detecting and analyzing clusters

Forecasting and visualizing future results

Using R functions and scripts to create visuals within Power BI

Developing Solutions for System Monitoring and Administration

Developing Solutions for System Monitoring and Administration

Creating a centralized IT monitoring solution with Power BI

Constructing a monitoring visualization and analysis layer

Importing and visualizing dynamic management view (DMV) data of SSAS and Power BI data models

Increasing SQL Server DBA productivity with Power BI

Providing documentation of Power BI and SSAS data models to BI and business teams

Analyzing performance monitor counters of the Microsoft on-premises data gateway and SSAS tabular databases

Analyzing Extended Events trace data with Power BI

Visualizing log file data from SQL Server Agent jobs and from Office 365 audit searches

Enhancing and Optimizing Existing Power BI Solutions

Enhancing and Optimizing Existing Power BI Solutions

Enhancing the scalability and usability of a data model

Revising DAX measures to improve performance

Pushing query processing back to source systems

Strengthening data import and integration processes

Isolating and documenting DAX expressions

Deploying and Distributing Power BI Content

Deploying and Distributing Power BI Content

Preparing a content creation and collaboration environment in Power BI

Managing migration of Power BI content between development, testing, and production environments

Sharing Power BI dashboards with colleagues

Configuring Power BI app workspaces

Configuring refresh schedules and DirectQuery connections with the on-premises data gateway

Creating and managing Power BI apps

Building email subscriptions into Power BI deployments

Publishing Power BI reports to the public internet

Enabling the mobile BI experience

Integrating Power BI with Other Applications

Integrating Power BI with Other Applications

Integrating Excel and SSRS objects into Power BI Solutions

Migrating a Power Pivot for Excel Data Model to Power BI

Accessing and analyzing Power BI datasets from Excel

Building Power BI reports into PowerPoint presentations

Migrating a Power BI Data Model to SSAS Tabular

Accessing MS Azure hosted services such as Azure Analysis Services from Power BI

Using Power BI with Microsoft Flow and PowerApps

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Assigning data formatting and categories

Two important metadata properties to configure for any column that will be visible on the Power BI report canvas are the data format and data category. The data formats should be consistent across data types and efficiently convey the appropriate level of detail. Data categories serve to enhance the data exploration experience by providing Power BI with information to guide its visualizations.

In this recipe, we set the data formats for dimension columns and measures. Additionally, geographical data category values are assigned to location columns to aid the visualization of this data.

How to do it...

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