10 Ways to Connect Your Data to Power BI (From Excel to APIs & Beyond)

As a data analyst, I’ve connected everything from ancient Excel files to real-time IoT sensors to Power BI. Choosing the right connection method can mean the difference between a slow, broken dashboard and a blazing-fast, auto-updating report.

Here’s your ultimate guide to Power BI data connections—with pros, cons, and use cases for each method.

1. Excel & CSV Files

Best for: Quick ad-hoc reports.
How to connect:

• Power BI Desktop: Get Data → Excel/Text/CSV.
  Pros:
  ✅ Easy for beginners.
  ✅ No server needed.
  Cons:
  ❌ Manual refreshes required.
  ❌ Limited scalability (>100K rows slows down).

2. SQL Databases (SQL Server, MySQL, PostgreSQL)

Best for: Centralized business data.
How to connect:

• Get Data → SQL Server (or MySQL/PostgreSQL).
• Enter server name + credentials.
  Pro Tip:
• Use DirectQuery for real-time data (but limits DAX functions).
• Use Import Mode for full Power BI features (but slower with big data).

3. Cloud Databases (BigQuery, Snowflake, Redshift)

Best for: Enterprise data warehouses.
How to connect:

• Get Data → Azure → Google BigQuery/Snowflake.
• Requires service account keys (JSON) or OAuth.
  Optimization:
• Push query processing to the cloud (SELECT * is a no-no).

4. Web APIs (REST & OData)

Best for: SaaS tools (Salesforce, Twitter, weather data).
How to connect:

• Get Data → Web → Enter API URL.
• For auth: Basic (username/password) or OAuth2.
  Example:

https://api.twitter.com/2/tweets/search/recent?query=PowerBI  

Watch Out:

• API rate limits (use Power Automate to batch calls).

5. SharePoint & OneDrive

Best for: Team collaboration files.
How to connect:

• Get Data → SharePoint Folder → Paste site URL.
  Pro Tip:
• Use Power Query parameters to dynamically point to new files.

6. Power BI Datasets (Dataflows & Data Marts)

Best for: Reusing cleaned data across reports.
How to connect:

• Get Data → Power BI datasets.
  Why it’s great:
• Build once, reuse everywhere (single source of truth).

7. Python & R Scripts

Best for: Advanced transformations.
How to connect:

• Get Data → Python/R script.
  Example (Python):

import pandas as pd  
df = pd.read_csv("https://example.com/data.csv")  
df['profit'] = df['revenue'] - df['cost']  

Use Cases:

• Web scraping.
• Machine learning predictions.

8. Azure Services (Blob Storage, Cosmos DB)

Best for: Cloud-native apps.
How to connect:

• Get Data → Azure → Blob Storage/Cosmos DB.
  Pro Tip:
• Use Azure Data Factory to orchestrate complex pipelines first.

9. ODBC/JDBC Drivers

Best for: Legacy systems (SAP, Oracle).
How to connect:

1. Install vendor driver (e.g., SAP HANA ODBC).
2. Get Data → ODBC → Configure DSN.
   Warning:

• Often requires IT team help to set up.

10. Streaming Datasets (IoT, PubNub, Azure Stream)

Best for: Real-time dashboards (e.g., factory sensors).
How to connect:

• Get Data → Streaming dataset → API URL.
  Example:

https://api.pubnub.com/v1/data-stream?channel=sensor1  

Key Setting:

• Enable “Historic data analysis” to keep past records.

Cheat Sheet: Which to Choose?

Pro Tips for Reliable Connections

1. Always test credentials in Power BI Service after publishing.
2. Use parameters to switch between dev/prod data sources.
3. Monitor refresh failures with Power BI’s “Refresh History.”

Real-World Example: Retail Chain

• Problem: 50 stores sent daily CSV sales reports via email.
• Solution:
  1. Power Automate saved files to OneDrive.
  2. Power BI connected to folder → auto-refreshed.
• Result: Reports went from 8 hours manual work → 5 minutes.

Free Resources

• Microsoft’s Data Connectors List
• API Connection Templates

Need help connecting a tricky data source? DM me!