How to Implement Incremental Refresh in Power BI

Refreshing large datasets in Power BI can become time-consuming and resource-intensive as data volume grows. If your reports are based on millions of rows of historical data, refreshing everything daily is neither efficient nor necessary.

This is where Incremental Refresh comes in. It allows Power BI to only refresh new or changed data, drastically improving performance and reducing load on your data source. In this blog, you’ll learn how to set up incremental refresh step-by-step—so your Power BI reports stay fast and efficient even with big data.

What Is Incremental Refresh in Power BI?

Incremental Refresh enables Power BI to load data in partitions, refreshing only the latest ones (e.g., the past 7 days) while keeping the older data static.

Why use it?

• –Performance Boost: Faster refresh times for large models
• –Reduced Load: Lighter queries on your data source
• –Efficient Storage: Reuses cached data already processed

Step 1: Define Parameters in Power Query

·  Open your report in Power BI Desktop (Pro or Premium workspace)

·  Go to Transform Data (Power Query Editor)

·  Create two parameters:

• –RangeStart (Date/Time)
• –RangeEnd (Date/Time)

·  Set default values (e.g., RangeStart = 01/01/2020, RangeEnd = 01/01/2021)

Step 2: Filter Your Data with These Parameters

1. Select your date column (e.g., OrderDate)
2. Apply a filter using:
   • OrderDate >= RangeStart
   • OrderDate < RangeEnd
3. Click Close & Apply

This tells Power BI what time range to load and eventually refresh incrementally.

Step 3: Enable Incremental Refresh in Data Model

1. In the Fields pane, right-click your table > Incremental Refresh
2. Enable toggle: Incremental refresh and real-time data
3. Define the settings:
   • Store data for the last X years/months
   • Refresh only data for the last Y days
4. Click Apply

📝 Example:

• -Store data for: 5 years
• -Refresh: Last 7 days

This configuration refreshes only the recent week of data each time, while keeping the rest intact.

Step 4: Publish to Power BI Service

1. Save and publish the report to a workspace in Power BI Service (must be Premium or Pro with Premium Per User enabled)
2. Go to Dataset Settings > Scheduled Refresh
3. Set up credentials and enable the scheduled refresh

✅ Done! You’ve now implemented incremental refresh.

Best Practices

• -Use Date/Date Time columns only for filtering and partitioning
• -Test your refresh setup with smaller data samples
• -Avoid heavy transformations in Power Query when working with large datasets
• -Monitor refresh logs from Power BI Service for performance tuning

To conclude, Incremental Refresh is a game-changer when it comes to handling large datasets in Power BI. It not only saves refresh time but also optimizes resource usage. By learning how to configure it properly, you can scale your reports with confidence and efficiency

Got a large dataset slowing down your Power BI refresh? Implement Incremental Refresh today and see the difference. Explore more Power BI performance tips in our blog series—or reach out for help setting up enterprise-grade models.

We hope you found this blog useful, and if you would like to discuss anything, you can reach out to us at transform@cloudfonts.com