📌 Introduction
Connecting to data is the first and most critical step in any Power BI project. Power BI supports hundreds of data sources, making it one of the most flexible BI tools available today.
In this post, you’ll learn how to:
- Connect Power BI to Excel, CSV, and Text files
- Connect to SQL Server and databases
- Pull data from web sources and APIs
- Understand Import vs DirectQuery vs Live Connection
This guide is ideal for beginners transitioning from Excel to Power BI.
📊 Types of Data Sources in Power BI
Power BI data sources fall into four main categories:
| Category | Examples |
|---|---|
| File-based | Excel, CSV, Text, XML |
| Database | SQL Server, MySQL, Oracle |
| Cloud | Azure SQL, SharePoint, Google Analytics |
| Online / Web | APIs, Web pages, REST endpoints |
📁 Connecting to Excel, CSV & Text Files
🔹 Connecting to Excel Files
Excel is the most common data source for Power BI users.
Steps:
- Open Power BI Desktop
- Click Get Data → Excel
- Select your file
- Choose tables or sheets
- Click Load or Transform Data
✅ Best for:
- Small to medium datasets
- Business users & analysts
- Financial and sales reporting
🔹 Connecting to CSV & Text Files
CSV and TXT files are lightweight and widely used.
Steps:
- Get Data → Text/CSV
- Select file
- Preview delimiter & data format
- Load or transform data
⚠️ Common issues:
- Incorrect delimiters
- Encoding problems
- Date format mismatches
🗄️ Connecting to SQL Server & Databases
Power BI integrates seamlessly with databases.
🔹 SQL Server Connection
Steps:
- Get Data → SQL Server
- Enter Server name
Choose:
- Windows Authentication
- Database Authentication
- Select Import or DirectQuery
- Load data
🔹 Other Supported Databases
- MySQL
- PostgreSQL
- Oracle
- Snowflake
- Azure SQL Database
✅ Best practice:
- Write optimized SQL queries
- Avoid
SELECT * - Pre-aggregate data when possible
🌐 Connecting to Web Data & APIs (Basic)
Power BI can pull data from web URLs and REST APIs.
🔹 Web Data (Simple)
Used for public web pages or downloadable datasets.
Steps:
- Get Data → Web
- Paste URL
- Choose Table or JSON output
- Transform data in Power Query
🔹 APIs (Beginner Level)
APIs usually return data in JSON format.
Example:
Power BI converts JSON into tables automatically using Power Query.
⚠️ Limitations:
- Authentication complexity
- API rate limits
- Requires Power Query transformations
🔄 Import vs DirectQuery vs Live Connection
Choosing the right connection mode is crucial for performance.
🔹 Import Mode
- Data is loaded into Power BI
- Fastest performance
- Supports full DAX functionality
✅ Best for:
- Small to medium datasets
- Offline analysis
❌ Not real-time
🔹 DirectQuery Mode
- Queries data directly from source
- Real-time data access
- Limited DAX & modeling features
✅ Best for:
- Large datasets
- Real-time dashboards
❌ Slower visuals
❌ Dependent on source performance
🔹 Live Connection
Used with:
- Power BI datasets
- Azure Analysis Services
- No data stored in Power BI
- Enterprise BI
- Centralized data models
❌ No Power Query
❌ No calculated columns
🔍 Comparison Table
| Feature | Import | DirectQuery | Live |
|---|---|---|---|
| Performance | ⭐⭐⭐⭐ | ⭐⭐ | ⭐⭐⭐ |
| Real-time | ❌ | ✅ | ✅ |
| DAX Support | Full | Limited | Limited |
| Data Size | Medium | Large | Enterprise |
📌 Best Practices for Data Connections
- Clean data at source level when possible
- Use Import unless real-time is required
- Optimize SQL queries
- Avoid unnecessary columns
- Use Power Query transformations wisely
🚀 What’s Next?
In the next part, we’ll cover:
👉 Part 4: Data Cleaning & Transformation Using Power Query
You’ll learn how to:
- Remove duplicates & nulls
- Change data types
- Merge and append tables
- Build clean, analysis-ready datasets
0 Comments
If you have any doubt please comment or write us to - datahark12@gmail.com