Power BI vs. Power Query: Understanding Key Differences

Power BI is a business analytics tool from Microsoft that helps users visualize and share insights from their data. It integrates with multiple data sources and provides interactive dashboards and reports. In this guide, you’ll see the comparison between Power BI vs Power Query.

You’ll also dive deep into Power Query, how and when to use it, its Pros and cons, and the difference between Power BI and Power Query.

Table of Contents:

1. How and When Can You Use It?
2. What is a Power Query?
3. How and When Can You Use It?
4. What is the Difference between Power BI and Power Query?
5. How to Use Power BI and Power Query?
6. How to Migrate Power Query to Power BI?
7. How to Visualize Data in Power BI?
8. What are the Pros and Cons of It?
9. Wrap Up

First…

How and When Can You Use It?

You can use the Power BI in cases like:

When to Use Power BI?

• Data Analysis and Reporting: It’s used for analyzing large datasets, and creating interactive reports and dashboards.
• Decision-Making: It’s used to support decision-making with real-time data and visual insights.
• Data Integration: For combining data from multiple sources for a comprehensive view.
• Data Visualization: To present data in an easily understandable format through graphs, charts, and maps.

How to Use Power BI?

• Connect to Data Sources: Import data from multiple sources like Excel files, web sources, cloud services, or databases.
• Transform Data: Use Power Query to shape, clean, and transform data.
• Create Visualizations: Build interactive dashboards and reports using a variety of data visualizations like pie charts, maps, and bar charts.
• Build Reports: Design detailed reports with multiple pages, and also incorporate various visual elements.
• Share and Collaborate: Publish reports to the Power BI service to share with others. You can also collaborate and create dashboards that update automatically.

What is a Power Query?

Definition: Power Query is a data connection technology used in Power BI, Excel, and other Microsoft tools. It allows users to connect to multiple data sources, transform and clean data, and load it into a worksheet or model.

How and When Can You Use It?

• Open Power Query:
  • In Power BI: To open Power Query Editor, navigate to the Home tab and click on “Transform Data.”
  • In Excel: Navigate to the Data Tab, click on “Get Data,” and select “From Other Sources.”
• Connect to Data:
  • Use “Get Data” to connect to multiple data sources like web pages, databases, cloud services, or files (Excel, CSV).
• Transform Data:
  • Use the Query Editor to perform transformations like sorting, filtering, pivoting/unpivoting columns, merging tables, and changing data types.
  • To apply the transformation, select options from the menu or use the “Advanced Editor” for more complex modifications.
• Load Data:
  • Once transformations are complete, click “Close & Load” (for Excel users), or “Close & Apply” (for Power BI users) to load the cleaned data into the worksheet or model.
• Refresh Data:
  • Set up data refresh schedules to ensure your reports and dashboard stay updated with the latest information.

When to Use Power Query?

• Data Preparation: Used for cleaning and shaping raw data before analysis or visualization.
• Automation: For automating repetitive data transformation tasks like regular updates or scheduled data refreshes.
• Data Modeling: Used for preparing data to fit specific analytical or reporting needs. This ascertains that the data structure aligns with your requirements.

What is the Difference between Power BI and Power Query?

Power BI and Power Query serve different but complementary roles in data analysis:

• Power BI is a business analytics tool that helps users visualize data and create interactive reports and dashboards.
• Power Query is a data connection and transformation tool used within Power BI, Excel, and other Microsoft products.

Which one is best?

Your choice will be largely dependent on your needs.

• Power BI is used for creating reports and dashboards. It offers extensive data visualization and interactive features. It’s best for analyzing data and sharing insights across teams.
• Power Query is used for data preparation and transformation. It’s a tool within Power BI and Excel used for connecting, cleaning, and shaping data before data analysis.

How to Use Power BI and Power Query?

Using Power BI

• Connect to Data: Use the “Get Data” option to import data from multiple sources.
• Transform Data: Clean and shape data in Power Query Editor if needed.
• Create Visualizations: Use Power BI’s drag-and-drop interface to create charts, maps, and graphs.
• Publish and Share: Publish reports to Power BI Service for sharing and collaboration.

Using Power Query

• Open Power Query: You can access Power Query in Power BI or Excel from the “Transform Data” or “Get Data” options.
• Connect to Data: Select and connect to data sources.
• Transform Data: Use the Query Editor to clean, shape, and transform data.
• Apply Changes: Save and apply the change to load the transformed data into your worksheet or model.

How to Migrate Power Query to Power BI?

Here are steps to help you migrate Power Query from Excel to Power BI:

• Launch the Power BI and start a new report.
• Navigate to the “Home” tab and click “Get Data.” Next, select “Excel” to import the workbook containing your Power Query queries.
• During the import, choose the sheets or tables that include your Power Query queries.
• To open the Power Query Editor, click “Transform Data.” Your queries will be displayed here.
• Review and adjust queries as needed. After all, there might be differences in data connections or formats.
• Apply changes and load the data into Power BI for further visualization and analysis.

How to Visualize Data in Power BI?

Stage 1: Logging in to Power BI

• Log in to Power BI.
• Enter your email address and click the “Submit” button.

• You are redirected to your Microsoft account.
• Enter your password and click “Sign in“.

• You can choose whether to stay signed in.

• Once done, the Power BI home screen will open.

Stage 2: Creating a Data Set and Selecting the Data Set to Use in Your Chart

• Go to the left-side menu and click the “Create” button.
• Select “Paste or manually enter data“.

• We’ll use the sample data below for this example.

Country	Revenue Stream	Revenue (in $)
USA	Digital Advertising Revenue	39,620,000
USA	Event Marketing Revenue	10,670,000
USA	Content Marketing Revenue	5,580,000
USA	Print & Outdoor Revenue	455,270
UK	Digital Advertising Revenue	40,710,000
UK	Event Marketing Revenue	24,770,000
UK	Content Marketing Revenue	6,330,000
UK	Print & Outdoor Revenue	552,190
DNK	Digital Advertising Revenue	47,040,000
DNK	Event Marketing Revenue	29,070,000
DNK	Content Marketing Revenue	7,740,000
DNK	Print & Outdoor Revenue	600,690
DNK	Media Relations Revenue	106,430
AUS	Digital Advertising Revenue	53,790,000
AUS	Event Marketing Revenue	38,530,000
AUS	Content Marketing Revenue	6,590,000
AUS	Print & Outdoor Revenue	9,040,000
AUS	Media Relations Revenue	6,130,000
FR	Digital Advertising Revenue	57,860,000
FR	Event Marketing Revenue	50,450,000
FR	Content Marketing Revenue	3,560,000
FR	Print & Outdoor Revenue	18,790,000
FR	Media Relations Revenue	15,460,000
IND	Digital Advertising Revenue	60,470,000
IND	Event Marketing Revenue	63,200,000
IND	Content Marketing Revenue	2,080,000
IND	Print & Outdoor Revenue	29,500,000
IND	Media Relations Revenue	30,020,000

• Paste the data table above into the “Power Query” window. Next, select the “Create a dataset only” option.

• Navigate to the left-side menu, and click on the “Data Hub” option. Power BI will populate the data set list. If no data set has been created, you’ll get an error message.

• Click on the “Create Report” dropdown.

• To add the Power BI Sankey Diagram Extension by ChartExpo, you’ll have to create the Sankey Diagram. That requires the use of an add-in or Power BI visual from Appsource. Navigate to the right side of the Power BI dashboard, and open the Power BI Visualizations panel. Click the ellipsis symbol (…) to import the Power BI Sankey Diagram extension by ChartExpo. On the menu that opens, select the “Get more visuals’ option.

• Enter “ChartExpo” in the highlighted search box. You’ll see the “Sankey Diagram for Power BI by ChartExpo” as shown below.

• Click on the Sankey Diagram. After that, click the highlighted “Add” button.

• Power BI will add the “Sankey Diagram for Power BI by ChartExpo” icon in the visualization panel.

• To draw a Sankey Diagram with ChartExpo’s Power BI extension, select the “Sankey Diagram for Power BI by ChartExpo” icon in the visualization panel. You’ll see a window that opens in the report section of the dashboard.

• You’ll have to select the fields to use in the Sankey chart.

• Follow the sequence below when selecting the fields:
  • Application Channels
  • Initial Screening
  • Conduct Interviews
  • Employee Onboarding
  • Total Candidates
• You’ll have to provide your email address or the ChartExpo License key. To see the Sankey Chart display of your data, you’ll have to add the key under the visual section.

• You can add the top header text in the chart under the General section as shown below:

• Click on Visual to set the number and also enable the options as shown below:

• You can change the “Node Font Style” as shown below:

• You can change the “Level Font Style.”

• You’ll see the Level Labels as shown below:

• You can change the Node color by following the steps below:

• You’ve seen “How to change Level 1 color.” You can follow the same step to change the node color of other levels.

• Here’s the final look of the HR Dashboard in Power BI using ChartExpo after changing all nodes’ colors.

Insights

• Social Media is the primary application channel with the highest number of candidates (1100).
• Company Career Page follows with 900 candidates.
• Events and Paper Media have comparatively lower candidate counts of 350 and 700, respectively.
• Direct applications have 600 candidates.

What are the Pros and Cons of It?

Pros of Power BI

• User-Friendly: It features an intuitive interface, and makes it accessible for users at different technical levels.
• Integration: It integrates seamlessly with other Microsoft products like Azure and Excel. This enhances workflow efficiency.
• Real-Time Updates: It provides real-time data monitoring and interactive dashboards. This will, in turn, aid timely data-driven decision-making.

Cons of Power BI

• Complex Advanced Features: Advanced functionalities and customizations can be complex and require technical expertise.
• Learning Curve: Mastering all features involves a significant learning curve.

FAQs

Can you connect Power Query to Power BI?

Yes, Power Query in Power BI is used to connect to, transform, and prepare data. You can access Power Query directly within Power BI by clicking “Transform Data” on the Home tab.

Is Power Query related to Power BI?

Yes, Power Query is integrated into Power BI. It’s used within Power BI to connect to, transform, and clean data before creating visualizations and reports.

Is Power BI better than Power Query?

Power BI and Power Query serve different purposes. Power BI excels in visualization and reporting, while Power Query is focused on data preparation and transformation. They complement each other.

Wrap Up

Comparing Power BI vs. Power Query is of the essence. It will help you understand that Power BI focuses on data visualization and reporting, while Power Query specializes in data connection, preparation, and transformation.

Power BI can integrate seamlessly with other Microsoft products like Azure and Excel. The free version offers substantial functionality but imposes restrictions on data capacity and refresh rates.

When it comes to analyzing large datasets and creating interactive reports and dashboards, Power BI is your best bet. It offers visual insights and helps users make decisions with real-time data. Furthermore, it helps in presenting data in an understandable format through maps, graphs, and charts.

For effective data evaluation, you should create dashboards in Power BI and also represent evolving data using visualizations like the Sankey chart.