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arock-well
Helper V
Helper V

Merging Queries: Dataflows vs Datasets?

I realize that normalized star/snowflake schemas are usually the best way to go in terms of efficiency and performance. However, I have a situation where I have six tables (one fact, six dimension) and I want to create into one big flat table.

 

In terms of merging these tables, is there any benefit when it comes to efficiency and refresh performance to do this in a Dataflow or later on in a Dataset? 

6 REPLIES 6
parry2k
Super User
Super User

@arock-well there are many reasons but 2-3 top would be: 1) it will take less time to refresh since data is already processed in the dataflow 2) better compression 3) dataflow processed data can be used in multiple Power BI reports 



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Thank you!

parry2k
Super User
Super User

Well in that case (for whatever reason) I would recommend it to do in dataflow. 



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Thanks! Are there particular reasons why you'd go the dataflow route vs dataset? 

parry2k
Super User
Super User

@arock-well first question, why a flat table if you have a proper star schema, you can potentially run into challenges using one flat table, and also it is not the best practice.



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As I already mentioned in the original message, I realize that is the best way to go. I'm not going to go into the reasons.

 

So based on that, what would be the best way to do merges: Within a dataflow or dataset?

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