Forum Discussion
What is system storage?
Anonymous
I would suggest optimizing data models, specifically for that super large model:)
Several reasons why the semantic models got inflated in Power BI services, one of the major reasons would be:
Columnar Storage Expansion. Power BI Service converts your model into a highly optimized VertiPaq columnar database. While this format is compressed for query performance, metadata (dictionaries, hierarchies, relationships) can inflate storage by 2–3× compared to the desktop file.
There are some other reasons like Data Partitioning etc.Regarding how to reduce the size of your model, you would need to optimize them in desktop tool with a couple of measures below:
- Remove unused columns, hide unused tables.
- Use integer/date keys instead of strings.
- Avoid high-cardinality columns (e.g., free-text).
- Switch to DirectQuery for large tables if feasible.these are very general approaches from internet and I'd recommend you refer to SQLBI for detailed guidance and best practices.
https://www.sqlbi.com/articles/optimizing-high-cardinality-columns-in-vertipaq/
Super UserAnonymous
I would suggest optimizing data models, specifically for that super large model:)
Several reasons why the semantic models got inflated in Power BI services, one of the major reasons would be:
Columnar Storage Expansion. Power BI Service converts your model into a highly optimized VertiPaq columnar database. While this format is compressed for query performance, metadata (dictionaries, hierarchies, relationships) can inflate storage by 2–3× compared to the desktop file.
There are some other reasons like Data Partitioning etc.
Regarding how to reduce the size of your model, you would need to optimize them in desktop tool with a couple of measures below:
- Remove unused columns, hide unused tables.
- Use integer/date keys instead of strings.
- Avoid high-cardinality columns (e.g., free-text).
- Switch to DirectQuery for large tables if feasible.
these are very general approaches from internet and I'd recommend you refer to SQLBI for detailed guidance and best practices.
https://www.sqlbi.com/articles/optimizing-high-cardinality-columns-in-vertipaq/