Power BI Modeling Challenge: Decimal Precision vs Model Size

Thankyou, srlabhe, for your response.

Hi bdpr_95,

We appreciate your enquiry through the Microsoft Fabric Community Forum.

Based on my understanding, the increased model size is not caused by decimal precision alone, it may also result from high cardinality and the use of the Double data type, both of which reduce VertiPaq compression efficiency. When decimal values have many unique values or long precision, the storage engine cannot compress them effectively, resulting in larger model sizes.

Please follow the steps below, which may help to resolve the issue:

1. Use Power BI Desktop’s Model view to identify which columns consume the most memory.
2. If precision permits, convert from Decimal (Double) to Fixed Decimal Number in Power Query, as it provides exact precision and often compresses more effectively.
3. Store values as scaled integers or pre calculate them at the source before loading into Power BI. Avoid using text unless absolutely necessary, as it further increases size.
4. Compare model size after each change using VertiPaq Analyzer to quantify the precision versus performance trade-off.

Additionally, please refer to the link below:
Data reduction techniques for Import modeling - Power BI | Microsoft Learn

We hope the information provided helps to resolve the issue. Should you have any further queries, please feel free to contact the Microsoft Fabric community.

Thank you.