Getting Data from Multiple Sources in Power BI: A Pictorial Guide to Seamless Data Integration

Madina Yusuff — Sat, 04 Apr 2026 11:08:17 +0000

Introduction

Power BI is a business intelligence and data visualization tool developed by Microsoft that enables users to connect to multiple data sources, transform raw data, and create interactive dashboards and reports.

It allows individuals and organizations to collect data from sources like Excel, databases, and cloud services; Clean and transform data using Power Query; Build visualizations such as charts, graphs, and maps; And share insights through reports and dashboards across teams.

In simple terms, Power BI helps turn raw data into meaningful insights for better decision-making.
In this guide, you will learn how to:
• Establish connections between Power BI and multiple data sources effectively
• Leverage Power Query to preview and examine datasets
• Identify and fix data quality issues at an early stage
• Create a solid foundation for accurate data modelling and reporting

Architecture Overview

At a general level, the Power BI data architecture includes:
• Power BI Desktop serving as the primary tool for reporting and data modelling
• A variety of data sources, such as:
o Excel and Text/CSV files
o SQL Server databases
o JSON and PDF files
o SharePoint folders
All data is imported into Power BI through Power Query, where it is inspected and transformed before being loaded into the data model.

Connecting Data from Multiple Sources

Power BI supports connections to numerous types of data sources. The following sections provide step-by-step instructions for each major source.

Step 1: Connecting to Excel

Launch Power BI Desktop
Go to Home → Get Data → Excel
Locate and select your Excel file
In the Navigator window, choose the necessary sheets or tables
Click Load (to import immediately) or Transform Data (to clean the data first)

Step 2: Connecting to Text/CSV File

Launch Power BI Desktop
Go to Home → Get Data → Text/CSV
Browse and select the CSV file (e.g., OYO_crosschecked.csv)
Review the dataset preview in the dialog box
Click Load or Transform Data

Step 3: Connecting to PDF

Launch Power BI Desktop
Go to Home → Get Data → PDF
Choose the PDF file
Allow Power BI to scan and detect available tables
Select the relevant table(s)
Click Load or Transform Data

Step 4: Connecting to JSON

Launch Power BI Desktop
Go to Home → Get Data → JSON
Select the JSON file or provide the API endpoint
Load the data into Power Query
Expand nested elements to properly structure the dataset
Click Close & Apply

Step 5: Connecting to SharePoint Folder

Launch Power BI Desktop
Go to Home → Get Data → SharePoint Folder
Input the SharePoint site URL
Click OK and complete authentication if prompted
Choose the files from the folder The option of a Sharepoint Folder requires you to have access to an internal Organizational account to have the sharepoint URL. The URL will look like https://contoso.sharepoint.com/sites/siteName.
Click Combine & Transform Data

Step 6: Connecting to MySQL Database

Launch Power BI Desktop
Go to Home → Get Data → MySQL Database
Enter the server name and database details
Provide the required login credentials
Select the desired tables
Click Load or Transform Data

Step 7: Connecting to SQL Server

Launch Power BI Desktop
Go to Home → Get Data → SQL Server
Enter the server name (e.g., localhost)
Leave the database field empty (or specify one if necessary)
Click OK
Select the authentication method (e.g., Windows credentials)
In the Navigator pane, expand the database (e.g., World)
Choose the required tables such as: o City o Country o CountryLanguage
Click Transform Data to open the Power Query Editor

Step 8: Connecting to Web Data

Launch Power BI Desktop
Go to Home → Get Data → Web
Enter the URL of the website or API
Click OK
Select the detected data table or structure
Click Load or Transform Data

Step 9: Connecting to Azure Analysis Services

Launch Power BI Desktop
Go to Home → Get Data → Azure → Azure Analysis Services
Enter the server name
Select the appropriate database or model
Choose the connection mode (Live connection is recommended)
Click Connect This structured approach enhances clarity, makes the guide more practical, and reflects real-world Power BI workflows.

Conclusion

Working with multiple data sources in Power BI goes beyond a simple technical task. It forms the backbone of every dependable and insight-driven report. As demonstrated, modern data ecosystems are diverse and complex, requiring analysts to integrate data from files, databases, cloud platforms, and web services seamlessly.
Power BI Desktop simplifies this process significantly. Its strong integration features, along with Power Query, allow you not only to access data but also to evaluate its structure, assess its quality, and understand its limitations.
However, the true benefit lies in what comes after data connection. The ability to detect inconsistencies, manage missing values, and transform raw data into a well-structured format distinguishes basic reporting from advanced analytics solutions.
In real-world applications, effective data ingestion leads to:
• Improved accuracy of insights
• Enhanced decision-making processes
• Scalable and maintainable data models
For any data professional, mastering data connectivity and preparation is essential. It ensures that every dashboard created is not only visually appealing but also reliable and meaningful.
Ultimately, successful analytics starts with high-quality data—and high-quality data depends on how well it is connected, prepared, and understood.
Power BI Desktop simplifies this process significantly. Its strong integration features, along with Power Query, allow you not only to access data but also to evaluate its structure, assess its quality, and understand its limitations.
However, the true benefit lies in what comes after data connection. The ability to detect inconsistencies, manage missing values, and transform raw data into a well-structured format distinguishes basic reporting from advanced analytics solutions.
In real-world applications, effective data ingestion leads to:
• Improved accuracy of insights
• Enhanced decision-making processes
• Scalable and maintainable data models
For any data professional, mastering data connectivity and preparation is essential. It ensures that every dashboard created is not only visually appealing but also reliable and meaningful.
Ultimately, successful analytics starts with high-quality data—and high-quality data depends on how well it is connected, prepared, and understood.

DEV Community: Madina Yusuff