Power BI Modeling Performance

Hi ericjo 

Hi,
Your current approach of using a very wide table with many columns (one for each time period or classification) introduces serious challenges:

Poor performance due to high column count and low compression

Increased file size (as you’ve already noticed)

Difficulty writing flexible DAX calculations

Complicated and fragile data model

 Recommended approach: Use a star schema.

In this structure:

You have one central Fact table with many rows and a narrow set of columns:

Store ID

Purchase Date

Product ID

Sales Amount / Quantity

Other metrics as needed

Around it, you define Dimension (Lookup) tables for:

Date (calendar)

Product

Customer

Store

Time zone, etc.

Important rule:
Each dimension table should contain one unique row per entity:

One row per product in the Product table

One row per date in the Calendar table

One row per customer in the Customer table
This ensures clean relationships and optimized performance.

This structure is the best practice for Power BI modeling, and will make your reports faster, smaller, and more maintainable.
For more guideness please relate :
https://learn.microsoft.com/en-us/power-bi/guidance/star-schema

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