Reduce Data Volume in Power BI: Filtering Rows for Better Performance

Описание: When reviewing a Power BI semantic model, one of the quickest wins is simple:

Stop loading rows that the report will never use.

Many models load far more data than is actually needed.
This increases model size, slows refresh, and puts unnecessary pressure on capacity.

In this video, I walk through practical ways to reduce the number of rows loaded into the model, including:

• Filtering out old or irrelevant historical data (dynamic date filtering in M)
• Limiting data to the actual region, division, or scope of the report
• Removing draft, cancelled, archived, duplicate, or test records
• Loading data at the level the report actually uses (instead of unnecessary transaction-level detail)
• Keeping only relevant and active entities

The principle is simple:

If the report will never use those rows, they should not be loaded.

I also demonstrate different ways to implement this in Power Query (M):
• Using filters directly in the UI
• Writing filtering logic in M code
• Choosing the right approach depending on the situation

This is part of a series on reviewing and improving Power BI semantic models, focusing on practical steps that improve performance without changing business logic.

#PowerBI #PowerQuery #SemanticModel #DataModeling #BusinessIntelligence