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Data modelling with dataflows

I'm wondering what is the best practice to model data with the use of dataflows.


My data source is Power BI dataflows created by the company, they contain a big amount of data - the fact table is huge. Also, dimension tables are quite big. To transform and filter data I used dataflows that I made.


For every report, I just need a part of the data - the problem is that the attributes that I need to filter by are mostly in dimension tables so then I need to filter using merges.


Due to how the connections between tables are constructed to filter by attributes present in Dimension 1 I need to go through Dimension 2 and then I can filter the Fact. This kind of transformations make the performance horrible, dataflows can take 2 hours to transform all the data and consume a lot of CPU. These kind of issues are not present when using SQL sources - usually joins are not that costly, transformations with Power Query are much less efficient.


I also have a question about dimensions - should I load them as a whole or filter to rows only relevant to my filtered fact table?


I have dataflows where there are 10+ merges. For now I have separate dataflows for staging and then usually one layer for transformations. Will making it more layered solve my problems? I read that incremental refresh when dataflow is a source is not recommended - why? Won't it improve performance of my dataflows?

Community Support
Community Support

Hi @Anonymous 

Thanks for reaching out to us.

To improve your model's performance, here are 2 articles for your reference,

Data Modelling In Power BI: Helpful Tips & Best Pr... - Microsoft Power BI Community

Best practice rules to improve your model’s performance | Microsoft Power BI Blog | Microsoft Power BI



Best Regards,

Community Support Team _Tang

If this post helps, please consider Accept it as the solution to help the other members find it more quickly.

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That is not helpful at all.

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