What is the difference in speed between import mode and direct mode of data flow?

[koya112929]

Hi,

I used Power BI Premium's Data Flow to create the report.

In Power BI Desktop, when I selected the dataflow from Get Data, the performance was different when I selected it in import mode and in DirectQuery.

When I used Import, the data could be aggregated in about a minute when I issued it to Service.
When using DirectQuery, the data timed out after about 5 minutes of publishing to Service.

When there was only one table, the performance did not change much, but when two tables were aggregated on the report, the performance of DirectQuery on the data flow did not change much.
The performance of the DirectQuery in the data flow seemed to drop when changing two tables on the report.

Is there any difference in performance between Import and DirectQuery?
If I want performance, should I use Import?

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thanks

[aj1973 · Accepted solution]

Hi @koya112929 

The difference between Direct query and Import mode is not the Performance. 

The choice of what and how you need to connect to your data and then in the service is what makes the difference.

Both modes have their pros and cons and limitations.

https://docs.microsoft.com/en-us/power-bi/connect-data/desktop-use-directquery

[koya112929]

Thank you for your quick answer!

I have solved the problem!

I have been able to access the dataflow from Power BI Desktop.
This is also the case for Power BI Premium dataflow.
I imagined that selecting this option would not change the performance of reports published to the Power BI Service, and that both would behave the same as import.

It seems that the data flow also works the same as the traditional import and Directquery.

Thank you very much.

[tomtang]

Hi @koya112929 

Interested in your question above.

As I am also testing the performance btw direct query and import when using PBI dataflow as the data source.

Do you find the performance is worse when connecting to dataflow in direct query mode? (compare to import mode)

Thanks!