Getting Data from Multiple Sources in Power BI: Building the Foundation of a Data Analytics Career

Dreez — Tue, 21 Apr 2026 11:25:09 +0000

Introduction

Every meaningful insight in data analytics begins long before dashboards and visualizations. It starts with how you access, understand, and prepare your data.

In Microsoft Power BI, data ingestion is not just a technical step; it is a core professional skill that separates entry-level users from confident, job-ready analysts.

Many beginners focus heavily on visuals and charts, colors, and layouts. But in real-world scenarios, the real challenge is different.

Data is most times scattered, inconsistent, incomplete, and often comes from multiple sources.

As a data analyst, you are expected to:

Work with Excel reports from finance
Pull structured data from SQL databases
Clean CSV exports from operational systems
Extract insights from APIs (JSON)
Integrate cloud-based sources like SharePoint or Azure

This is where Power BI becomes a powerful ally. With Power BI Get Data
functionality and Power Query Editor, you are equipped not just to connect to data, but to:

Explore and understand its structure
Detect quality issues early
Transform raw data into analysis-ready datasets

Connecting Data from Multiple Sources

Power BI provides a wide range of connectors that allow you to integrate diverse data sources into a single analytical model. In real-world analytics, data comes from multiple systems, including:

Excel and CSV files
SQL Server and MySQL databases
JSON APIs and web sources
PDFs and SharePoint folders
Cloud platforms like Azure

What You Will Learn in This Guide

This guide is designed with your career growth in mind. By the end, you will be able to Confidently connect to multiple data sources in Power BI such as

Excel
Text/CSV Files
PDF
JSON
SharePoint Folder
MySQL Database
SQL Server
Web Data
Azure Analysis Services

1. Connecting to Excel

Open Power BI Desktop

Navigate to Home → Get Data → Excel

Browse and Select your Excel file

In the Navigator window, choose tables or sheets

Click Load(to import directly) or Transform Data(to clean data before import)

💡 Best Practice: Always structure your Excel data as tables and avoid merged cells.

2. Connecting to Text/CSV Files

Open Power BI Desktop

Navigate to Home → Get Data → Text/CSV

Select your CSV file (e.g., data.csv promomtion)
Preview the dataset

Click Load or Transform Data

💡 Key Learning: Check column data types immediately (text vs numeric).

3. Connecting to PDF

Go to Home → Get Data → PDF → Connect

Select the PDF file and click open

Allow Power BI to detect tables

Select required table(s)

Click Load or Transform Data

💡 PDFs often require additional cleaning due to unstructured formats.

4. Connecting to JSON

Navigate to Home → Get Data → JSON → Connect

Select to load file or API endpoint

Open in Power Query

Expand nested structures into columns

Select the fields that you want to load into Power BI Desktop, clear the Use original column name as prefix checkbox, and then select OK.

Click the Alphabet/Numeral icon to the left of the column headers to verify/change data type

Review the selected data to ensure that you're satisfied with it, then select Close & Apply to load the data into Power BI Desktop.

💡 JSON requires understanding of hierarchical data structures.

5. Connecting to SharePoint Folder

Go to Home → Get Data → More

SharePoint Folder → Connect

Enter SharePoint site URL

Note; You don’t need to paste the full file path or link. Just enter the main website URL. Once you connect, you’ll be able to choose the specific list or data you want to load. If your link is too long, simply remove the extra part at the end and keep only the main site address (as shown in the example).

Authenticate using any medium you have access too and connect

Select files

Click Combine & Transform Data

💡 Widely used in corporate environments for collaborative data storage.

6. Connecting to MySQL Database

Navigate to Home → Get Data → MySQL Database → Connect

Enter server and database

Authenticate (Select a sign-in option, enter your username and password, and then select Connect.) When connecting to a database, you may see different authentication options:

Windows – Use your Windows login (the same one you use to sign in to your computer or Azure Active Directory).
Database – Use credentials specific to the database. For example, SQL Server can have its own username and password. If your database administrator gave you a separate login, enter it here.
Microsoft account – Use your personal Microsoft account. This is usually for connecting to Azure services or cloud database

Select tables

Click Load or Transform Data

💡 Databases provide structured, reliable data for analysis.

7. Connecting to SQL Server

Go to Home → Get Data → SQL Server

Enter server name (e.g., localhost)

Click OK

Authenticate (Select a sign-in option, enter your username and password, and then select Connect.) When connecting to a database, you may see different authentication options:

Windows – Use your Windows login (the same one you use to sign in to your computer or Azure Active Directory).
Database – Use credentials specific to the database. For example, SQL Server can have its own username and password. If your database administrator gave you a separate login, enter it here.
Microsoft account – Use your personal Microsoft account. This is usually for connecting to Azure services or cloud database

Expand database (e.g., parks_and_recreation)

Select table(s):

Click Transform Data (prepare data as desired)

💡 Professional Tip:
Always inspect data in Power Query before loading — never assume it is clean.

8. Connecting to Web Data

Navigate to Home → Get Data → Web

Enter URL and click OK

Select any of the detected tables you want

Click Load or Transform Data

💡 Ideal for APIs and publicly available datasets.

9. Connecting to Azure Analysis Services

Go to Home → Get Data → Azure → Azure Analysis Services

Connect

Enter server name

Choose Live Connection and click OK

Click Connect

💡 Used in enterprise environments with pre-built semantic models.

Conclusion

Connecting to multiple data sources in Power BI is more than a technical capability, it is a career-defining competency.

In today’s data-driven world, organizations rarely operate on a single source of truth. Data is distributed across systems, formats, and platforms. As an analyst, your value lies in your ability to bring that data together, make sense of it, and prepare it for decision-making.

Power BI simplifies this complexity, but it does not remove the responsibility. That responsibility belongs to you the analyst.

By mastering data connection techniques,** data preview and profiling, _early detection of data quality issues**_

You are building something far more important than reports. You are building analytical confidence and professional credibility.

💡

Final Career Perspective:
Tools will evolve. Interfaces will change. But the ability to understand and integrate data from multiple sources will remain one of the most valuable and transferable skills in your analytics journey.

DEV Community: Dreez