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Hello,
I have some data in Snowflake tables based on which I want to create a Power BI Report.
* Fact table is on the transaction level and has 90M rows, each column has integers only (as it shoudl be in the fact table)
* There are approx 15 dim tables (integers, strings, dates)
* I will create a view for each table with WHERE conditions to narrow down the data scope
* All data transformations will be done on the Snowflake level
* I will create Dataflows to connect to these views in Snowflake (One Dataflow = One View)
* Dataflows will be refreshed automatically once per day
What I am considering is:
* Import Mode
* Reducing cardinality of string columns as much as possible
* Loading only tables and columns that are used in the report
* Using booleans wherever possible
* Setting up a dedicated Virtual Warehouse in Snowflake with XL (or bigger size)
* As a last resort - purchasing P1 or higher SKU
I suppose that bigger VW in Snowflake will result in data being refreshed faster in Dataflow, but as this is an import mode project, the duration of refresh will not impact end-users anyway (I think).
What are the other checks I should consider?
Thanks!
@DataGuest , if you use dataflow and again import data in power bi dataset(Import mode), then you will duplicate data. So use dataflow only for dimensions not for fact. Import Fact in file. Limit the data on power bi desktop, Only load on the service
Dataflows and Dataset Design Pattern implementation: https://youtu.be/zwhJ1hWPcrA
Premium- Deployment Pipeline, Load More Data on Test/Prod : https://youtu.be/l69cnWkoGX0
Guyincube- 3 ways to reduce data- https://www.youtube.com/watch?v=qZOEDBedATA
refer if needed
Best Practices
https://maqsoftware.com/expertise/powerbi/power-bi-best-practices
https://docs.microsoft.com/en-us/power-bi/guidance/power-bi-optimization
https://www.c-sharpcorner.com/article/power-bi-best-practices-part-1/
https://www.knowledgehut.com/blog/business-intelligence-and-visualization/power-bi-best-practices
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