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showy's avatar
showy
Icon for Helper II rankHelper II
1 year ago
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Performance Boost repetitive Measures

I have a data model that consists Dim_Item, a Dim_FAC_Storage and a fact table called ItemLedger. These tables are well-structured. One special feature in the Itemtable is that each article has 9 le...
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  • v-echaithra's avatar
    v-echaithra
    1 year ago

    Hi showy ,

    When working with a large-scale Power BI data model including a fact table with ~20 million records and ~1 million unique items  managing dynamic pricing levels (Level10 to Level90) using separate measures can significantly degrade performance and maintainability. To improve performance and simplify your model, it’s essential to consolidate logic, minimize measure duplication, and reduce computational overhead during visuals rendering.
    1. Normalize Pricing Levels in Power Query
    2. Create a Disconnected Slicer Table for Price Levels
    3. Write a Single Dynamic Measure

    Hope this helps.
    Warm regards,
    Chaithra E.

v-echaithra's avatar
v-echaithra
Icon for Community Support rankCommunity Support
1 year ago

Hi showy ,

When working with a large-scale Power BI data model including a fact table with ~20 million records and ~1 million unique items  managing dynamic pricing levels (Level10 to Level90) using separate measures can significantly degrade performance and maintainability. To improve performance and simplify your model, it’s essential to consolidate logic, minimize measure duplication, and reduce computational overhead during visuals rendering.
1. Normalize Pricing Levels in Power Query
2. Create a Disconnected Slicer Table for Price Levels
3. Write a Single Dynamic Measure

Hope this helps.
Warm regards,
Chaithra E.