Refresh Performance with Snowflake

Hi manoj_0911 If your question is about improving the refresh performance in general, then you can check the below .

1. Query folding -> ensure that your query is getting folded for each table
2. Bring only necessary columns from the source 
3. Push the transformation to the Source side , avoid implementing transformations in the PQ layer / Dax layer e.g calculated columns 
4. Implement incremental refresh where ever possible 
5. If you can add the fact tables in Direct query mode and the create a user managed aggreagates (in incremental refresh)
6. Take care of the normal design standards data type and precesions like keep data time as date if the time is not helpfull , use integer for joins etc
7. enable large model in service and query scaling 

Thanks 

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1. Push transformations to Snowflake

Try to keep Power Query as simple as possible and let Snowflake do the heavy work.

Good approach:

• Use views or optimized SQL queries in Snowflake

• Avoid complex transformations in Power Query

2. Use Incremental Refresh

For large tables, this is usually the biggest performance improvement.

Configure incremental refresh with parameters such as: "RangeStart", "RangeEnd"

3. Ensure query folding

Power BI should push filters and transformations back to Snowflake. In Power Query: "Right click step → View Native Query". If folding is broken early, Power BI may pull large datasets locally before processing.

4. Reduce imported data

Only import what you actually need:

• remove unused columns

• filter historical data

• avoid importing high-cardinality text columns when unnecessary

Reducing data size improves both refresh time and model compression.

5. Avoid unnecessary table relationships during load

Large models with many relationships can slow processing.

• prefer star schema

• avoid complex many-to-many relationships

• use surrogate keys when possible