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I have revenue and loss data imported into Power BI. I have two options for creating a new column for profit:
First, create it as a calculated column in the query. Second, create a new column with DAX
My question: is there a performance difference, or any advice in which is best practice?
Solved! Go to Solution.
Hi @Anonymous ,
This is a list of the most important differences between DAX calculated columns and Power Query computed columns considering specific scenarios.
You can also refer these articles and videos which introduce the difference in details, hope it could help:
Best Regards,
Community Support Team _ Yingjie Li
If this post helps, then please consider Accept it as the solution to help the other members find it more quickly.
Hi @Anonymous ,
This is a list of the most important differences between DAX calculated columns and Power Query computed columns considering specific scenarios.
You can also refer these articles and videos which introduce the difference in details, hope it could help:
Best Regards,
Community Support Team _ Yingjie Li
If this post helps, then please consider Accept it as the solution to help the other members find it more quickly.
If you do it in M and store it as a column the model, assuming it will have a high cardinality, it will take up a lot of room in memory for no benefit. The Vertipaq engine is designed to do the calculations in my example very quickly even on large data sets.
@Anonymous
If your Profit is simply Revenue - Loss then your best option is to do it with measures.
Revenue Measure = SUM ( YourTable[Revenue] )
Loss Measure = SUM ( YourTable[Loss] )
Profit Measure = [Revenue Measure] - [Loss Measure]
If working with big data, is there any difference in the performance of doing it in the query with M language versus DAX? Curious to learn more about how Power BI works
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