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First Interaction with Power BI Desktop:

Power BI is an awesome visualization tool that helps in getting data from various data sources, cleaning the raw and dirty data, creating relationships between data models, and displaying the data into amazing visualizations.

When you first open Power BI Desktop, a window pops up.

This is the welcome screen. You can access Recent Data sources, Get Data icon, Power BI Blogs, Tutorials, Forums, etc. from here.

Power BI Desktop: On the top left side of the screen, there is a small rectangle which is a save button. It looks like this:

Below are Re-do and Un-do Buttons.

This is the main ribbon on the Desktop.

These are explained under:


When you click on File menu, you will be able to see the below options:

New: This opens a new Power BI window which allows us to start a new Project.

Save and Save As: It allows us to save the current work as PBIX file in our local machine. Whereas ‘Save’ button saves the change we made in already existing file, ‘Save As’ creates a new PBIX file or saves the existing file to a new location.

Get Data: This button allows us to import Data from various sources. The list of Data sources to which Power BI can connect to, is huge.

Import: It allows us to import Power BI template, Power BI visual from file and AppSource.

Export: It allows us to export Power BI template and Export to PDF

Publish: This allows us to publish the report to Power, which is a cloud-based service.

Options and Settings:



Data Load: It allows us to control the settings while loading the data into Power BI Desktop such as Type Detection, time intelligence and background data.

Power Query Editor: We can deal with settings of Power Query Editor with this tab such as enabling/ disabling intellisense of M language.

Direct Query: It gives the option to control Direct Query from SAP Hana Database.

R Scripting: This gives the options to choose the Integrated Development Environment (IDE) of R to launch in Power BI Desktop and the location to store R script in local machine. R script is used to prepare data models and to create reports in Power BI, after a connection is established between R and Power BI Desktop.

Python Scripting: This gives the options to choose the Integrated Development Environment (IDE) of Python to launch in Power BI Desktop and the location to store Python in local machine. Python is used in Power BI for data cleaning and other transformations. Python is used for creating visualizations as well.

Security: This gives security options for Data extensions, custom visuals, authentication browser and a few other things.

Privacy: This gives the option to combine data according to privacy level settings for each source or each file or ignore the privacy level settings.

Regional Settings: It controls settings for Application and Model language.

Updates: This allows to enable/ disable update notifications of Power BI Desktop.

Usage Data: If enabled, information is collected about how the Power BI Desktop is used.

Diagnostics: This feature, when started, runs evaluations caused by the user. ‘Diagnose step’ runs an evaluation of a single step that the user is trying to investigate.

Preview Features: This gives a list of features in the latest version of Power BI Desktop to enable/disable.

Auto Recovery: When anyone creates a Power BI Model, a copy of this file is saved in Auto Recovery and the file can be accessed from the auto recovery file location, if the file is closed without saving.

Report Settings: This provides settings for visuals, accessibility, page alignment and Format pane in the canvas area of the report page.

Current File:

Data Load: It controls the settings of how the data will look like when imported, such as detection of column types and headers, auto detect relationships, parallel loading of tables, etc.

Regional Settings: It controls language settings for text of current file.

Privacy: It gives control for privacy for each data source to the user. The user has the option to combine data according to privacy level for each source or to ignore the privacy level for source data. There are three types of Privacy levels in Power BI- Organizational, Public and Private.

Auto Recovery: Enable/ disable auto recovery of current files.

Published Data Set: It gives control to the end user to edit SAP variables in Power BI Premium and shared workspaces. It can only be used with direct query connections.

Query Reduction: It gives control on number of queries by disabling cross highlight/ cross filter, control on slicers and filters .

Report Settings: This gives the option to enable/disable personalized visuals, export data settings, etc.

Data Source Settings:

Get Started: This button opens the startup Power BI welcome screen.


This allows us to bring Data from various sources like Excel workbooks, Power BI Datasets, Dataflows, Dataverse, SQL Server, Text files, etc. Selected sheets will be available as ‘Table Query Objects’ in Power BI.

This allows you to select Excel worksheets to bring into Power BI Desktop. The selected sheets become table query objects in Power BI.

Data Hub Items:

Power BI Datasets: You can use Power BI datasets in your organization for which you have build permission / Read only permissions, with this tab. Read only permissions have limited access to the user.

DataMart (Preview): Data marts are web-based, built in visuals which integrates with Power BI and is used for self-service data analytics. Data marts are available for premium or Premium Per User (PPU) licenses. One single Data Mart can contain multiple data sources.

It allows you to connect to SQL Server Database and bring the required data into Power BI Desktop. The imported tables become Table Query Objects.

It allows you to create a new Table inside Power BI. This table is loaded as a Table Query object inside Power BI Desktop to perform further calculations/ visualizations.

It allows you to connect to Dataverse Environment domain by copying and pasting the URL of Environment Domain. The data connectivity can be Import or Direct. To get to that environment, go to From there, you will get the list of all the environments you are working in. You can select the domain name, by clicking the environment you want to connect with, copy the URL of that domain and paste it in Power BI Desktop Dataverse window.

This will list out all the recent sources from where you have extracted the data. You can bring in additional data in Power BI desktop from recent sources.

If you click on table like structure on top of this button, it will open Power Query Editor window, which helps in cleaning, and perform further calculations with the help of M language.

If you click on drop down in this button, it will give you two options:

1. Transform data- Go to Power Query Editor Window as defined earlier.

2. Data Source Settings- Allows you to change the source data in your local machine if there is a change in the location of the source file.

This updates the Table Query object if there are changes in source data.

Visualization Pane:

This Pane allows you to select the visual you want to display your data in. It contains Bar charts, Column charts, Line charts, Area charts, Slicer visual, Pie chart, Donut chart, Treemap visual, etc. From the dropdown, you can also import custom visuals from local machine as well as from the web.

This creates a text box inside the canvas area, to provide headings, subheadings, etc. along with the visuals.

As the name suggests, it allows you to import more visuals from your local machine or AppSource, if you want to display the data in other than already provided visuals.

When you click on this button, a dialogue box will open. This allows you to perform calculations based on the needs of the model for reports. For example, if net price and unit cost is given in the table, we can calculate the amount of sales by using the values of these columns. The created measures can be used in the report to display the calculated measure value. The language used in calculating ‘New Measure’ is DAX (Data Analysis Expressions). The new measure is displayed as an explicit measure with a calculator symbol in the list of columns, under the table for which this measure is created. The new measure dialogue box can also be opened when we right click on Table Query Objects on the right side of the Power BI Desktop in Report view.

Quick measures are for people who are not very comfortable in writing DAX queries, especially the beginners. If enabled, it gives the option to calculate the required value easily. The user can specify necessary values for calculating the new value and Power BI takes care of writing DAX for you. The DAX generated behind the scenes can be looked at by clicking the new measure created under the table.

Sensitivity labels can be applied in Power BI Desktop (Reports) and Power BI Service (Datasets, Dataflows, Reports and Dashboards,). The sensitivity labels must be enabled for tenant to use. Admin can define the users who can apply sensitivity labels. Only Pro or Premium per user licensed people, who are a part of the security group, can apply a sensitivity label.

This allows you to publish the report in Power BI service, which is a cloud-based service and is a part of Power BI Suite.


This allows you to add a blank page or make a duplicate of the existing page in your report. Both actions can also be performed by clicking on the plus sign and Right-clicking on page names simultaneously, under the canvas area.

This opens a window inside the canvas area from which you can select the type of visuals you want to display your data in.

Already explained under HOME.

When you click on this button, a window pops up in the canvas area which allows you to build a visual based on the values you provide. Q&A can be set up by the report developer, by going to settings of Q&A to add more synonyms, so this feature can respond better to people’s questions.

This visual is used if you want to analyze the important factors which are behind a particular metric. For example, which products bring in the highest level of sales, which characteristics in a teacher motivate a student to learn more, etc. Key influencers visual has AI features enabled.

It is a type of visual which also analyzes a particular metric with respect to its influences based on the other factors. For Example, we can analyze company sales with respect to its influences by category and location. It gives us step by step analysis of a particular value.

If enabled, this gives you a written text highlighting the important points in a report/ visual. It has some limitations such as cross filtering. It cannot be connected live with the SQL server Analysis Services (SSAS) and Azure Analysis Services. Also, it cannot be pinned to a dashboard. There are a few other limitations as well.

This feature allows you to insert paginated reports from Power BI service (Pro or Premium Per User capacity) in your Desktop Report. The paginated report is seen as a separate visual in canvas area. The Paginated Report visual doesn’t interact with the other visuals in the desktop if there are no parameters defined in the Paginated Report and vice versa. In other words, Paginated Report brought in the Power BI Desktop can be both static and dynamic, depending on the parameters.