Power BI Incremental Refresh: Handle Millions of Rows Faster

Infinite Data, Instant Reports: The Power of Incremental Refresh

If you're waiting 20 minutes for your Power BI dashboard to refresh every morning, you're not managing data—you're being held hostage by it.

The Problem: The "Full Refresh" Bottleneck

When your dataset grows into the millions of rows, a standard full refresh becomes a nightmare. It strains your source system, consumes excessive memory, and creates massive downtime. Refreshing 10 years of historical data just to update today's sales is a massive waste of resources.

The Risk: Frequent full refreshes on large datasets often lead to timeout errors and high costs in premium capacities.

The Solution: Incremental Refresh

Incremental refresh solves this by only loading the data that has changed. By partitioning your data into "Historical" and "Refresh" windows, Power BI keeps the old data static and only pulls the newest records. This reduces refresh times from minutes to seconds.

Pro Tip: Ensure your data source supports Query Folding. If Power BI can't "fold" the filter back to the database, incremental refresh won't provide the performance boost you're looking for.

Setup Checklist

1. Create RangeStart and RangeEnd parameters (Date/Time).
2. Filter your fact table using these parameters.
3. Right-click the table and select "Incremental Refresh".
4. Define your "Store" period and your "Refresh" period.

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