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When should I lean on a specific solution instead of the other one?
Which one is the least computationally heavy?
Solved! Go to Solution.
Hi @Anonymous ,
Normally, the column performance in Power Query will be better than the calculated column in DAX.
Because a column with better compression is smaller in memory and usually provides better performance levels. This is important in filter, group, and aggregation operations involving the column. The compression of a DAX calculated column might be lower than that of a Power Query computed column.
So if you can do it in Power Query/M, you should (except when you are adding a column to a table that references a column in a different table).
If a calculated column or a measure will work, use a measure.
Best Regards,
Stephen Tao
If this post helps, then please consider Accept it as the solution to help the other members find it more quickly.
Hi @Anonymous ,
Normally, the column performance in Power Query will be better than the calculated column in DAX.
Because a column with better compression is smaller in memory and usually provides better performance levels. This is important in filter, group, and aggregation operations involving the column. The compression of a DAX calculated column might be lower than that of a Power Query computed column.
So if you can do it in Power Query/M, you should (except when you are adding a column to a table that references a column in a different table).
If a calculated column or a measure will work, use a measure.
Best Regards,
Stephen Tao
If this post helps, then please consider Accept it as the solution to help the other members find it more quickly.
Please see this article. However, you should use a measure as your first option unless you definitely need a column (for an axis, legend, etc.). In practice, there isn't a big difference for most models but I typically do it in the query if I can (so it is compressed upon loading).
Comparing DAX calculated columns with Power Query computed columns - SQLBI
Pat
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