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a user told something weird, i'd like to have your feeling
Usecase : appending two tables
I thought the process would be like :
But i've been told that the append process take a long time like if :
Would you confirm scenario 1 or 2 ?
Thanks
Steph
Solved! Go to Solution.
Hello @Anonymous
the queries tab1 and tab2 should no be imported in your data model, so they are not loaded nowhere. But as you are refering to both of them in your combine-query they are loaded. I think that if loading 1 single tab takes 15 min, the append process will take almost that time x 2
BR
Jimmy
@Anonymous ,
Not sure on this but you can try something like this.
If you don't need to load the tables, then you can append as new table or you can append Table 1 with Table 2 and disable the load on Table 2. So that your refreshing time will be quicker and Data size will be lesser.
regards,
Manikumar
Proud to be a Super User!
Thanks Manikumar,
if i unactivate the load, it'd still import the data and it wouldn't go down into my model.
So my question would still be :
-import tab1 [15 min] and don't load into the model
-import tab2 [15 min] and don't load into the model
- append <1min or 30 min ?
I could deactivate the actualisation but i do want my data to be refreshed.
Hello @Anonymous
the queries tab1 and tab2 should no be imported in your data model, so they are not loaded nowhere. But as you are refering to both of them in your combine-query they are loaded. I think that if loading 1 single tab takes 15 min, the append process will take almost that time x 2
BR
Jimmy
@Anonymous ,
While appending on the Power query and as we are doing it on Top 1000 rows or some number of rows, It will be quick on power query but when you Close&Apply the changes it has to refresh both the datasets and perform append.
So it would be slightly more than the normal time like 20-25 mins, I would say.
Regards,
Manikumar
Proud to be a Super User!
Good to know : when executing an append, it doesn't rely on previous imports, it re-executes the imports.
So this is scenario #2 :
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