🔄 Power BI Incremental Refresh

Introduction In our last post, we established that Import Mode is the default powerhouse for Power BI — fast, flexible, and fully featured. But what happens when your data grows beyond what a full refresh can handle? Refreshing 200 million rows of sales history every night is slow, expensive, and fragile. One gateway timeout and your entire dashboard goes stale.

Incremental Refresh is Power BI’s answer to this problem. Instead of reloading everything, it only processes the data that has actually changed. In this post, we will break down exactly how it works and how to configure it step by step.

What Is Incremental Refresh?

Incremental Refresh splits your dataset into two invisible partitions:

• Historical partition: A large, frozen chunk of old data that is never touched after the initial load (e.g., everything older than 3 months).
• Refresh partition: A small, rolling window of recent data that gets refreshed on every scheduled run (e.g., the last 3 days).

Instead of reloading 200 million rows nightly, Power BI only reloads the 50,000 rows from the last 3 days. The result is dramatically faster refreshes, lower gateway load, and a much more reliable pipeline.

The Two Magic Parameters

Incremental Refresh is powered by exactly two reserved Power Query parameters that Power BI recognises by name. You must name them precisely:

Power BI uses these parameters to automatically partition your data. You define them — Power BI manages them at refresh time.

How to Configure Incremental Refresh: Step by Step

Step 1: Create the Parameters in Power Query

1. Open Power Query Editor (Home → Transform Data)
2. Go to Manage Parameters → New Parameter
3. Create the first parameter:
   • Name: RangeStart
   • Type: Date/Time
   • Current Value: 1/1/2024 12:00:00 AM
4. Create the second parameter:
   • Name: RangeEnd
   • Type: Date/Time
   • Current Value: 6/3/2026 12:00:00 AM

Step 2: Filter Your Date Column Using the Parameters

1. In Power Query, select your main fact table (e.g., Sales)
2. Click the dropdown on your date column (e.g., OrderDate)
3. Choose Date/Time Filters → Custom Filter
4. Set the filter: OrderDate is after or equal to RangeStart AND before RangeEnd
5. Click OK and close Power Query

Step 3: Define the Incremental Refresh Policy

1. In the Data or Model view, right-click your fact table
2. Select Incremental Refresh
3. Toggle Incremental Refresh on
4. Configure the two windows:
   • Archive data starting: 3 Years
   • Refresh data in the last: 3 Days
5. Click Apply

Step 4: Publish to Power BI Service

Incremental Refresh policies only activate after you publish to the Power BI Service. The partitioning and partition management happen in the cloud, not in Desktop.

Final Summary: When to Reach for Incremental Refresh

Incremental Refresh sits in the sweet spot between full Import Mode and DirectQuery. Use it when your data is too large for a nightly full refresh but does not require true real-time updates. It gives you the speed of Import Mode with the scalability to handle years of historical data without ever touching a row that has not changed.