Quicksight Dataset Replacement Data type mismatch Decimal

Hello, I am replacing a dataset in Quicksight and it’s complaining with an error: mismatch datatype. Both the old and the new underlying table has double precision as data type. I checked and also both the old dataset and the new dataset is also showing as decimal. Why is it complaining?

hi @sirish_bhatta - Can you please give a screenshot of the error and also check the data type of the file as well.

If there is a mismatch in the data type, the import is possibly not working, Give the details we will check and help in solution.

Tagging QuickSight experts as well for their feedback.
@Max @Bhasi_Mehta @Thomas @Tatyana_Yakushev @Ashok @Biswajit_1993

Regards - San

The error states:
The following mismatches were found

column_x ! mismatched type

hi @sirish_bhatta, I think this things happened when your data value is not correct. Even if your data type is fine but if your value in the field is not correct the it is showing you error.

In my suggestion please check once with data value for both old & new datasets and if any thing is there then fix it and then try to replace.

Thanks & Regards
Biswajit Dash

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Hi @sirish_bhatta - Please check @Biswajit_1993 advise whether you have any out of range value available in data. For example if you are defining a column data type as int but the data is coming as float, it will throw an error. I know it is a pain. What is your data source, is a relational or file based. Is it possible to create a new data set and import this data and see the data type, then you can understand the differences.

Hope this will help you.

Regards - San

Hi, I don’t know if there was any changes related to handling double precision within Quicksight done recently I had checked and tried pushing in different datatypes: numeric, decimal with different precision and scale. Nothing worked.
I ended up deleting that colum from the source, which allowed me to ignore that field and then I was able to add that field. Hope QS had a default ignore option without going this route. But it worked that way. Just had to go through some trial and error.


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Thanks @sirish_bhatta - Cool, it is a learning process but surely the problematic column data type w.r.t data is not matching and you have to fix it at record level.

Once the data is imported, you can ignore. However if you are having a relation database source, you can use custom sql and select only require columns. Since the issue is resolved, please mark any suggestion as solution so that this blog can archive.

Regards - San