Merging with one exact match column and one fuzzy join column

[ianbruckner]

Is it possitble to do a merge based on two columns... one that requires the first column selected to be an exact match, and on a second column with fuzzy join settings applied?

I'm trying to merge a dataset that, when there are overlaps, one of the columns should ALWAYS be an exact match (system date)... but the second column I'm matching on is free-form text field. I want to avoid fuzzy matches from that second column matching up to a result when the dates from the first column don't match. I've tried my best to rig it, by making the date field a Long-form text version of date, so the chances of a bad match because both columns are fuzzy matched being low, but I've found it not exact, and playing with the similarity threshold hasn't solved the problem. 

[LaasyaS]

Came across this thread when I had the same issue. 

Worked around by creating a separate intermediate mapping table. 
Group/Aggregate both the tables you are trying to compare by only 1 metric - the field that you are trying to fuzzy match. 
Then fuzzy match the 2 grouped tables to give you a conversion table. Play with the similarity etc as needed until you're satisfied with the result. 
Then join the conversion table to the first table to give you the field needed from the second table. 

Now you can do an exact match between the first and second table on both fields. 

[ImkeF · Accepted solution]

Hi @ianbruckner ,
I'm afraid, but the merge/join-condition can only be set on the join level and not on column-level.

For inner joins, you can simply do them one after another, but for outer joins you have to expand the matched columns and then apply some logic afterwards (taking only those rows, where both expansions returned values).

[ianbruckner]

Thanks, @ImkeF. Your answer of possible or not was helpful... and your solution of double merging is leading me to consider my datasets more closely to see if there might be other similarties I can code against.