How to Connect MySQL to Power BI Using ODBC

Joy Ikwue — Tue, 07 Apr 2026 06:56:31 +0000

Introduction

In today’s data-driven world, the ability to connect and analyze data from different sources is a key skill for any data professional. One common requirement is integrating relational databases like MySQL with powerful visualization tools such as Microsoft Power BI.

While Power BI provides a native MySQL connector, there are situations where using an ODBC (Open Database Connectivity) connection is more flexible, especially in enterprise environments or when dealing with compatibility issues.

In this guide, I’ll walk through how to connect MySQL to Power BI using the ODBC driver, step by step.

What You Will Learn
By the end of this tutorial, you will be able to:

Understand what ODBC is and why it’s used
Install and configure the MySQL ODBC driver
Set up a Data Source Name (DSN)
Connect Power BI to MySQL using ODBC
Troubleshoot common connection issues

Requirements
Before we begin, make sure you have:

A running MySQL database (local or remote)
Access credentials (username, password, host, port)
Microsoft Power BI Desktop installed
MySQL ODBC Driver installed

Step 1: Install MySQL ODBC Driver
First, download and install the MySQL ODBC driver (also known as MySQL Connector/ODBC).

Visit the official MySQL website (https://www.mysql.com/)
Click on Download
Scroll down, click MySQL Community (GPL) Downloads
Click Connector/ODBC
Download the correct version (32-bit or 64-bit — must match your Power BI bit) note: ODBC is for 64-bit
Complete the installation

Step 2: Configure ODBC Data Source (DSN)

Once installed, you need to create a Data Source Name (DSN).
Open ODBC Data Source Administrator
Press Windows + S → Search ODBC Choose:
Click on run as administration
A box pop-up will show
Click Add
A box pop-up will show
Select MySQL ODBC Driver
A box pop-up will show
Fill in the connection details:
Data Source Name: (e.g., MySQL_PBI)
Server: your MySQL host (e.g., localhost or IP)
Port: usually 3306
User: root
Password: input your MySQL password (note: the password used in opening your MySQL is the same password you will input)
Database name: your MySQL database name (e.g., moniespoint_DB)
Click Test to confirm connection (if successful click OK, if not successful, is mean the password you input is incorrect)
Save the DSN

Step 3: Connect Power BI to MySQL via ODBC
Now let’s bring the data into Power BI.

Open Microsoft Power BI Desktop
Click Get Data
Click More
Click Other
Select ODBC and click connect
Choose your DSN (e.g., MySQL_PBI)
Enter credentials if prompted
Select tables or write a SQL query
Click Load or Transform Data

Step 4: Transform and Visualize Data

Once your data is loaded:
Use Power Query Editor to clean and transform
Create relationships between tables
Build dashboards using charts, KPIs, and slicers

Common Issues & Fixes
Here are some typical problems and how to solve them:

Driver Mismatch (32-bit vs 64-bit): Ensure Power BI and ODBC driver use the same architecture
Connection Failed: Check host, port, and credentials Ensure MySQL server is running
Firewall Issues: Allow port 3306 on your server
Authentication Errors: Confirm MySQL user permissions

Why Use ODBC Instead of Native Connector?

Works across multiple database types
More control in enterprise environments
Useful when native connectors fail
Standardized connection method

Conclusion
Connecting MySQL to Microsoft Power BI using ODBC is a reliable and flexible approach, especially when working in complex or restricted environments.

With the steps outlined above, you can now:

Set up your ODBC driver
Establish a secure connection
Load and transform data in Power BI
Build meaningful dashboards

As a data analyst, mastering connections like this expands your ability to work with diverse data sources and deliver impactful insights.

Getting Data from Multiple Sources in Power BI

Joy Ikwue — Tue, 31 Mar 2026 03:31:16 +0000

Introduction

The foundation of every successful Power BI report is reliable data ingestion. No matter how visually appealing your dashboards are, poor or inconsistent data will always lead to misleading insights.

In real-world scenarios, data rarely comes from a single source. As a data analyst, you’ll often work with Excel files, CSVs, SQL databases, APIs, PDFs, and cloud platforms, all within the same report. Power BI simplifies this complexity through its powerful Get Data and Power Query features.

In this guide, you will learn how to:

Connect Power BI to multiple data sources efficiently
Use Power Query to explore and preview data
Identify and resolve data quality issues early
Build a solid foundation for accurate data modelling and reporting

Architecture Overview

A typical Power BI data architecture includes:

Power BI Desktop for modeling and reporting
Multiple data sources such as:

Excel and CSV files
SQL databases
JSON and PDF files
SharePoint folders

All data flows through Power Query, where it is cleaned and transformed before loading into the data model.

Connecting to Multiple Data Sources

Power BI supports a wide range of connectors. Below is a simplified guide:

Step 1: Connecting to Excel

Open Power BI Desktop
Navigate to Home → Get Data → Excel
Browse and select your Excel file
In the Navigator window, select the required sheets or tables
Click Load (to import directly) or Transform Data (to clean first)

Step 2: Connecting to Text/CSV Files

Open Power BI Desktop
Navigate to Home → Get Data → Text/CSV
Browse and select the CSV file
Preview the dataset in the dialog window
Click Load or Transform Data

Step 3: Connecting to PDF

Open Power BI Desktop
Navigate to Home → Get Data → PDF → Connect
Select the PDF file
Wait for Power BI to detect available tables
Select the desired table(s)
Click Load or Transform Data

Step 4: Connecting to JSON

Open Power BI Desktop
Navigate to Home → Get Data → JSON
Select the JSON file or input API endpoint
Load the data into Power Query
Expand nested fields to structure the data properly
Click Close & Apply

Step 5: Connecting to SharePoint Folder

Open Power BI Desktop
Navigate to Home → Get Data → SharePoint Folder
Enter the SharePoint site URL
Click OK and authenticate if required
Select files from the folder
Click Combine & Transform Data

Step 6: Connecting to MySQL Database

Open Power BI Desktop
Navigate to Home → Get Data → More → Database → MySQL Database
Enter the server name and database
Provide authentication credentials
Select the required tables
Click Load or Transform Data

Step 7: Connecting to SQL Server

Open Power BI Desktop
Navigate to Home → Get Data → SQL Server
Enter the server name (e.g., localhost)
Leave the database field blank (or specify one if needed)
Click OK
Select authentication method (e.g., Windows credentials)
In the Navigator pane, expand the database
Select required tables
Click Transform Data to open Power Query Editor

Step 8: Connecting to Web Data

Open Power BI Desktop
Navigate to Home → Get Data → Web
Enter the URL of the web page or API
Click OK
Select the data table or structure detected
Click Load or Transform Data

Step 9: Connecting to Azure Analysis Services

Open Power BI Desktop
Navigate to Home → Get Data → Azure → Azure Analysis Services
Enter the server name
Select the database/model
Choose connection mode (Live connection recommended)
Click Connect This structure improves readability, makes your blog actionable, and aligns well with real-world Power BI workflows.

Conclusion

Connecting to multiple data sources in Power BI is more than a technical process. It forms the foundation of every reliable and insight driven report. As highlighted, modern data environments are diverse and complex, requiring analysts to seamlessly work across files, databases, cloud services, and web platforms.

Power BI Desktop simplifies this complexity through its strong integration capabilities. When combined with Power Query, it enables not only data access but also a deeper understanding of data structure, quality, and limitations.

The true value, however, goes beyond simply connecting data. The ability to identify inconsistencies, manage missing values, and transform raw data into a clean and structured format is what distinguishes basic reporting from impactful analytics solutions.

In practice, effective data ingestion leads to

More accurate insights
Improved decision making
Scalable and maintainable data models.

As a Data professional, mastering data connectivity and preparation is essential. It ensures that every dashboard created is not only visually compelling but also reliable and meaningful.