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So we have a data set called Cases.

This data set has many records with same case id(duplicates) but different service frequency, for example, yearly, monthly, quarterly.

Case table data

Case_Id    Service_Frequency
123        Monthly
123        Yearly
345        Quarterly

So here comes the requirement, calculate the percentage of # of cases with different frequency against total # of unique cases.

By referring above table, the result should be grouped by service fequency

Service_Frequency   #_of_case  #total_uniqie_case  Percentage
Monthly             1          2                   50%
Quarterly           1          2                   50%
Yearly              1          2                   50%

By using compare table, I can successfully get # of case with each Service frequency, but I couldn't find way to work out sum of unique cases by using running total or sliding windowing function, Compare table in Einstein:

Service_frequency    #_of_cases        Total_count
Monthly              1                 3
Quarterly            1                 3
Yearly               1                 3

Total count windowing function: (sum(sum(#_of_cases)) over ([..] partition by all ) as 'Total_count')

Apparently, 3 is not I am working for, I need the number of total unique case based on case id, which 2. Is there a way to achieve this in compare table?

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