I am trying to update certain record set a variety of fields- if the Work_Type field is set as "Contingent" , I want to pull in the Field1, Field2, Field3 from another dataset and replace those fields in the original. In most cases they are blank in Dataset 1.
The field I am doing the "lookup" on is called UserID in data set 1 and ManagerID in dataset 2.
For example:
Dataset1
UserID | ManagerID | Field 1 | Field2 | Field3 | Worker_Type |
123 | 111 | Contigent | |||
234 | 222 | Contigent | |||
456 | 333 | Contigent | |||
789 | 444 | Contigent |
Dataset2
ManagerID | Field 1 | Field2 | Field3 |
111 | abc | abc | abc |
222 | xyz | xyz | xyz |
333 | bbb | bbb | bbb |
444 | aaa | aaa | aaa |
Result
UserID | ManagerID | Field 1 | Field2 | Field3 |
123 | 444 | aaa | aaa | aaa |
234 | 222 | xyz | xyz | xyz |
456 | 333 | bbb | bbb | bbb |
789 | 111 | abc | abc | abc |
Any suggestons? I am not having success with the Join or Find Replace tools.
Thanks.
Adam
Solved! Go to Solution.
I was thinking that approach but I thought it was too simple... :)
However, I only want to replace the values if the Worker Type is a certain value. Would that require a formula to determine which values from the new columns to use?
OR - should I do that process of just those worker types a different flow and then join it back?
Adam
Either approach will work, and I'm not sure which would perform better. If one does perform better, I'm guessing the difference would be minimal for small datasets (less than around a million rows).