PowerBI - Comparing two similar sets of data (Many to Many)











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I am trying to compare to sets of data that are very similar. I have done a bridge relation and used M:M relationship on PowerBI but I am still not getting the result I want.



Here is an example of the data:
Dataset 1



Name | Service | Usage 
A | 1 | 10
A | 2 | 20
B | 1 | 10
B | 2 | 10
C | 1 | 20
C | 2 | 10


Dataset 2



Name | Service | Usage 
A | 1 | 40
A | 2 | 20
B | 1 | 40
B | 2 | 10
C | 1 | 40
C | 2 | 10


Desired output



Name | Service | Usage 1 | Usage 2  
A | 1 | 10 | 40
A | 2 | 20 | 20
B | 1 | 10 | 40
B | 2 | 10 | 10
C | 1 | 20 | 40
C | 2 | 10 | 10


Is this possible in PowerBI?










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  • Instead of a bridge, create 2 dimensions: one with names. and one with services, and connect them to both tables (as 1:M relations). Then you will be able to drill across easily.
    – RADO
    Nov 21 at 23:35















up vote
0
down vote

favorite












I am trying to compare to sets of data that are very similar. I have done a bridge relation and used M:M relationship on PowerBI but I am still not getting the result I want.



Here is an example of the data:
Dataset 1



Name | Service | Usage 
A | 1 | 10
A | 2 | 20
B | 1 | 10
B | 2 | 10
C | 1 | 20
C | 2 | 10


Dataset 2



Name | Service | Usage 
A | 1 | 40
A | 2 | 20
B | 1 | 40
B | 2 | 10
C | 1 | 40
C | 2 | 10


Desired output



Name | Service | Usage 1 | Usage 2  
A | 1 | 10 | 40
A | 2 | 20 | 20
B | 1 | 10 | 40
B | 2 | 10 | 10
C | 1 | 20 | 40
C | 2 | 10 | 10


Is this possible in PowerBI?










share|improve this question
























  • Instead of a bridge, create 2 dimensions: one with names. and one with services, and connect them to both tables (as 1:M relations). Then you will be able to drill across easily.
    – RADO
    Nov 21 at 23:35













up vote
0
down vote

favorite









up vote
0
down vote

favorite











I am trying to compare to sets of data that are very similar. I have done a bridge relation and used M:M relationship on PowerBI but I am still not getting the result I want.



Here is an example of the data:
Dataset 1



Name | Service | Usage 
A | 1 | 10
A | 2 | 20
B | 1 | 10
B | 2 | 10
C | 1 | 20
C | 2 | 10


Dataset 2



Name | Service | Usage 
A | 1 | 40
A | 2 | 20
B | 1 | 40
B | 2 | 10
C | 1 | 40
C | 2 | 10


Desired output



Name | Service | Usage 1 | Usage 2  
A | 1 | 10 | 40
A | 2 | 20 | 20
B | 1 | 10 | 40
B | 2 | 10 | 10
C | 1 | 20 | 40
C | 2 | 10 | 10


Is this possible in PowerBI?










share|improve this question















I am trying to compare to sets of data that are very similar. I have done a bridge relation and used M:M relationship on PowerBI but I am still not getting the result I want.



Here is an example of the data:
Dataset 1



Name | Service | Usage 
A | 1 | 10
A | 2 | 20
B | 1 | 10
B | 2 | 10
C | 1 | 20
C | 2 | 10


Dataset 2



Name | Service | Usage 
A | 1 | 40
A | 2 | 20
B | 1 | 40
B | 2 | 10
C | 1 | 40
C | 2 | 10


Desired output



Name | Service | Usage 1 | Usage 2  
A | 1 | 10 | 40
A | 2 | 20 | 20
B | 1 | 10 | 40
B | 2 | 10 | 10
C | 1 | 20 | 40
C | 2 | 10 | 10


Is this possible in PowerBI?







powerbi






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edited Nov 21 at 21:21

























asked Nov 21 at 21:04









Heng Yon

11




11












  • Instead of a bridge, create 2 dimensions: one with names. and one with services, and connect them to both tables (as 1:M relations). Then you will be able to drill across easily.
    – RADO
    Nov 21 at 23:35


















  • Instead of a bridge, create 2 dimensions: one with names. and one with services, and connect them to both tables (as 1:M relations). Then you will be able to drill across easily.
    – RADO
    Nov 21 at 23:35
















Instead of a bridge, create 2 dimensions: one with names. and one with services, and connect them to both tables (as 1:M relations). Then you will be able to drill across easily.
– RADO
Nov 21 at 23:35




Instead of a bridge, create 2 dimensions: one with names. and one with services, and connect them to both tables (as 1:M relations). Then you will be able to drill across easily.
– RADO
Nov 21 at 23:35












2 Answers
2






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up vote
0
down vote













One approach (as suggested in comments), is to separate the distinct Name and Service values into separate dimension tables, in the query editor:



Names:



= Table.FromList(List.Distinct(List.Combine({#"Dataset 1"[Name], #"Dataset 2"[Name]})),Splitter.SplitByNothing(),{"Name"})


Services:



= Table.FromList(List.Distinct(List.Combine({#"Dataset 1"[Service], #"Dataset 2"[Service]})),Splitter.SplitByNothing(),{"Service"})


Create the DAX measures you want:



Usage 1 = SUM ( 'Dataset 1'[Usage] )



Usage 2 = SUM ( 'Dataset 2'[Usage] )



Now create relationships between the fact tables (Dataset 1, Dataset 2) and the dimension tables (Names, Services):



enter image description here



Then simply layout the visual as required:



enter image description here






share|improve this answer




























    up vote
    0
    down vote













    Another approach may be to combine your dataset fact tables into one table, with an added "dataset" column:



    Create your "combined" table in the query editor.



    Combined Table:



    = Table.Combine({Table.AddColumn(#"Dataset 1", "Dataset", each "Dataset 1", type text), Table.AddColumn(#"Dataset 2", "Dataset", each "Dataset 2", type text)})


    Now use this table as your single source - either with a crosstab visual:



    enter image description here



    Or by adding separate measure for each dataset:



    Usage 1 = CALCULATE ( SUM('Combined Data'[Usage]), 'Combined Data'[Dataset] = "Dataset 1" )



    Usage 2 = CALCULATE ( SUM('Combined Data'[Usage]), 'Combined Data'[Dataset] = "Dataset 2" )



    enter image description here






    share|improve this answer





















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      2 Answers
      2






      active

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      2 Answers
      2






      active

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      active

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      up vote
      0
      down vote













      One approach (as suggested in comments), is to separate the distinct Name and Service values into separate dimension tables, in the query editor:



      Names:



      = Table.FromList(List.Distinct(List.Combine({#"Dataset 1"[Name], #"Dataset 2"[Name]})),Splitter.SplitByNothing(),{"Name"})


      Services:



      = Table.FromList(List.Distinct(List.Combine({#"Dataset 1"[Service], #"Dataset 2"[Service]})),Splitter.SplitByNothing(),{"Service"})


      Create the DAX measures you want:



      Usage 1 = SUM ( 'Dataset 1'[Usage] )



      Usage 2 = SUM ( 'Dataset 2'[Usage] )



      Now create relationships between the fact tables (Dataset 1, Dataset 2) and the dimension tables (Names, Services):



      enter image description here



      Then simply layout the visual as required:



      enter image description here






      share|improve this answer

























        up vote
        0
        down vote













        One approach (as suggested in comments), is to separate the distinct Name and Service values into separate dimension tables, in the query editor:



        Names:



        = Table.FromList(List.Distinct(List.Combine({#"Dataset 1"[Name], #"Dataset 2"[Name]})),Splitter.SplitByNothing(),{"Name"})


        Services:



        = Table.FromList(List.Distinct(List.Combine({#"Dataset 1"[Service], #"Dataset 2"[Service]})),Splitter.SplitByNothing(),{"Service"})


        Create the DAX measures you want:



        Usage 1 = SUM ( 'Dataset 1'[Usage] )



        Usage 2 = SUM ( 'Dataset 2'[Usage] )



        Now create relationships between the fact tables (Dataset 1, Dataset 2) and the dimension tables (Names, Services):



        enter image description here



        Then simply layout the visual as required:



        enter image description here






        share|improve this answer























          up vote
          0
          down vote










          up vote
          0
          down vote









          One approach (as suggested in comments), is to separate the distinct Name and Service values into separate dimension tables, in the query editor:



          Names:



          = Table.FromList(List.Distinct(List.Combine({#"Dataset 1"[Name], #"Dataset 2"[Name]})),Splitter.SplitByNothing(),{"Name"})


          Services:



          = Table.FromList(List.Distinct(List.Combine({#"Dataset 1"[Service], #"Dataset 2"[Service]})),Splitter.SplitByNothing(),{"Service"})


          Create the DAX measures you want:



          Usage 1 = SUM ( 'Dataset 1'[Usage] )



          Usage 2 = SUM ( 'Dataset 2'[Usage] )



          Now create relationships between the fact tables (Dataset 1, Dataset 2) and the dimension tables (Names, Services):



          enter image description here



          Then simply layout the visual as required:



          enter image description here






          share|improve this answer












          One approach (as suggested in comments), is to separate the distinct Name and Service values into separate dimension tables, in the query editor:



          Names:



          = Table.FromList(List.Distinct(List.Combine({#"Dataset 1"[Name], #"Dataset 2"[Name]})),Splitter.SplitByNothing(),{"Name"})


          Services:



          = Table.FromList(List.Distinct(List.Combine({#"Dataset 1"[Service], #"Dataset 2"[Service]})),Splitter.SplitByNothing(),{"Service"})


          Create the DAX measures you want:



          Usage 1 = SUM ( 'Dataset 1'[Usage] )



          Usage 2 = SUM ( 'Dataset 2'[Usage] )



          Now create relationships between the fact tables (Dataset 1, Dataset 2) and the dimension tables (Names, Services):



          enter image description here



          Then simply layout the visual as required:



          enter image description here







          share|improve this answer












          share|improve this answer



          share|improve this answer










          answered Nov 22 at 10:25









          Olly

          3,0071926




          3,0071926
























              up vote
              0
              down vote













              Another approach may be to combine your dataset fact tables into one table, with an added "dataset" column:



              Create your "combined" table in the query editor.



              Combined Table:



              = Table.Combine({Table.AddColumn(#"Dataset 1", "Dataset", each "Dataset 1", type text), Table.AddColumn(#"Dataset 2", "Dataset", each "Dataset 2", type text)})


              Now use this table as your single source - either with a crosstab visual:



              enter image description here



              Or by adding separate measure for each dataset:



              Usage 1 = CALCULATE ( SUM('Combined Data'[Usage]), 'Combined Data'[Dataset] = "Dataset 1" )



              Usage 2 = CALCULATE ( SUM('Combined Data'[Usage]), 'Combined Data'[Dataset] = "Dataset 2" )



              enter image description here






              share|improve this answer

























                up vote
                0
                down vote













                Another approach may be to combine your dataset fact tables into one table, with an added "dataset" column:



                Create your "combined" table in the query editor.



                Combined Table:



                = Table.Combine({Table.AddColumn(#"Dataset 1", "Dataset", each "Dataset 1", type text), Table.AddColumn(#"Dataset 2", "Dataset", each "Dataset 2", type text)})


                Now use this table as your single source - either with a crosstab visual:



                enter image description here



                Or by adding separate measure for each dataset:



                Usage 1 = CALCULATE ( SUM('Combined Data'[Usage]), 'Combined Data'[Dataset] = "Dataset 1" )



                Usage 2 = CALCULATE ( SUM('Combined Data'[Usage]), 'Combined Data'[Dataset] = "Dataset 2" )



                enter image description here






                share|improve this answer























                  up vote
                  0
                  down vote










                  up vote
                  0
                  down vote









                  Another approach may be to combine your dataset fact tables into one table, with an added "dataset" column:



                  Create your "combined" table in the query editor.



                  Combined Table:



                  = Table.Combine({Table.AddColumn(#"Dataset 1", "Dataset", each "Dataset 1", type text), Table.AddColumn(#"Dataset 2", "Dataset", each "Dataset 2", type text)})


                  Now use this table as your single source - either with a crosstab visual:



                  enter image description here



                  Or by adding separate measure for each dataset:



                  Usage 1 = CALCULATE ( SUM('Combined Data'[Usage]), 'Combined Data'[Dataset] = "Dataset 1" )



                  Usage 2 = CALCULATE ( SUM('Combined Data'[Usage]), 'Combined Data'[Dataset] = "Dataset 2" )



                  enter image description here






                  share|improve this answer












                  Another approach may be to combine your dataset fact tables into one table, with an added "dataset" column:



                  Create your "combined" table in the query editor.



                  Combined Table:



                  = Table.Combine({Table.AddColumn(#"Dataset 1", "Dataset", each "Dataset 1", type text), Table.AddColumn(#"Dataset 2", "Dataset", each "Dataset 2", type text)})


                  Now use this table as your single source - either with a crosstab visual:



                  enter image description here



                  Or by adding separate measure for each dataset:



                  Usage 1 = CALCULATE ( SUM('Combined Data'[Usage]), 'Combined Data'[Dataset] = "Dataset 1" )



                  Usage 2 = CALCULATE ( SUM('Combined Data'[Usage]), 'Combined Data'[Dataset] = "Dataset 2" )



                  enter image description here







                  share|improve this answer












                  share|improve this answer



                  share|improve this answer










                  answered Nov 22 at 10:36









                  Olly

                  3,0071926




                  3,0071926






























                       

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