Get certified for free when you join Fabric Data Days 2026 and dive into Fabric, Power BI, SQL, AI, and other essential data skills.
Join nowJuly 7 - July 17 | Round 2 of the Power BI Dataviz World Championships. Don't miss your chance! Learn more
Hello,
could someone help me to refresh this query faster. It takes almost 15 mins to give me a result.
I'm using the filter function at the end because i got redundant lines with zero value for each column which is strange for me and i guess if you would help me out to get rid of these lines without filtering down after, it will make the refreshing super faster.
Thank you for your help!
let
Source = Table.NestedJoin(#"HISTORIQUE MB52", {"CLE", "Date"}, #"UPS STOCK", {"material_Sloc", "Date"}, "UPS STOCK", JoinKind.LeftOuter),
#"UPS STOCK développé" = Table.ExpandTableColumn(Source, "UPS STOCK", {"Account", "Ref 5 ", "Designator", "UPS QTY"}, {"Account", "Ref 5 ", "Designator", "UPS QTY"}),
FilterRows = Table.SelectRows(#"UPS STOCK développé", each [UNR] <> 0 or [Blocked] <> 0 or [Returns] <> 0 or [UPS QTY] <> null)
in
FilterRows
Solved! Go to Solution.
Hi @09 ,
Difficult to say without understanding the actual data and source.
Whilst there are a number of ways to speed up merges when the data and source are understood, my first recommendation will always be: don't do the merge at all. Merging is very expensive in Power Query (obviously excluding when folded to source), so I would do the following:
1) In both Table1 (#"HISTORIQUE MB52") and Table2 (#"UPS STOCK") create a merged column, called something like 'CLE_Date'. In Table1 this will be [CLE] and [Date] merged together, in Table2 it will be [material_Sloc] and [Date]. In both I would also add a delimiter, such as '-', between them to help avoid false positives.
2) Remove any columns from Table2 that you don't need, keeping at least your new [CLE_Date] column.
3) Send both tables to the data model and relate Table1[CLE_Date] to Table2[CLE_Date].
Pete
Proud to be a Datanaut!
Hi @09 ,
Difficult to say without understanding the actual data and source.
Whilst there are a number of ways to speed up merges when the data and source are understood, my first recommendation will always be: don't do the merge at all. Merging is very expensive in Power Query (obviously excluding when folded to source), so I would do the following:
1) In both Table1 (#"HISTORIQUE MB52") and Table2 (#"UPS STOCK") create a merged column, called something like 'CLE_Date'. In Table1 this will be [CLE] and [Date] merged together, in Table2 it will be [material_Sloc] and [Date]. In both I would also add a delimiter, such as '-', between them to help avoid false positives.
2) Remove any columns from Table2 that you don't need, keeping at least your new [CLE_Date] column.
3) Send both tables to the data model and relate Table1[CLE_Date] to Table2[CLE_Date].
Pete
Proud to be a Datanaut!
Join us in Barcelona for FabCon and SQLCon, the Fabric, Power BI, SQL, and AI community event. Save €200 with code FABCMTY200.
Join Data Days 2026: 60 days of free live/on-demand sessions, challenges, study groups, and certification opportunities.