QuickSight: Thousand Separator Formatting for Amount Stored as String

[redyampavan]

In the AWS QuickSight report, both the Value Type and Amount columns are defined as string data types. The Value Type column contains multiple distinct values, and for each value type, we need to display the corresponding Amount with a thousands (1,000) separator.

Since the Amount column is also a string, is it possible to achieve thousands Separator formatting in this scenario?

[RajKavuda]

@redyampavan

In the AWS QuickSight report, both the Value Type and Amount columns are defined as string data types. The Value Type column contains multiple distinct values, and for each value type, we need to display the corresponding Amount with a thousands (1,000) separator.

Since the Amount column is also a string, is it possible to achieve thousands Separator formatting in this scenario?

Hi @redyampavan ,

Applying thousands separator is possible once you data type of the Amount column from String to Decimal.

1. Go to your dataset
2. Find the Amount column
3. Change its data type from String to Decimal/Integer
4. Save and refresh your analysis
5. Then apply thousand separator formatting in the visual

Raj Kavuda

AWS Team

[Brett]

Hi @redyampavan,
One additional thing to add to the response above, if your field needs to stay as a string, you could building an ifelse statement as a work around but this may be limited on the number of comma’s that can be added. For your ifelse statement, I would suggest looking further into the two functions ‘Strlen’ and ‘substring’. Strlen can help in recognizing the string length, so if it’s longer than 3, you can start adding commas.

[redyampavan]

@RajKavuda

Applying thousands separator is possible once you data type of the Amount column from String to Decimal.

1. Go to your dataset
2. Find the Amount column
3. Change its data type from String to Decimal/Integer
4. Save and refresh your analysis
5. Then apply thousand separator formatting in the visual

Thanks for the suggestion.

In our case, the Amount column is intentionally maintained as a String, because we are using Value Type–based dynamic logic where multiple value types (numbers, currency, etc.) are handled within a single calculated field. Due to this design, converting the Amount column from String to Decimal/Integer is not feasible at the dataset level.

[redyampavan]

@Brett

One additional thing to add to the response above, if your field needs to stay as a string, you could building an ifelse statement as a work around but this may be limited on the number of comma’s that can be added. For your ifelse statement, I would suggest looking further into the two functions ‘Strlen’ and ‘substring’. Strlen can help in recognizing the string length, so if it’s longer than 3, you can start adding commas.

Thanks for the additional suggestion.

Yes, we did consider using an ifelse-based workaround with functions like strlen and substring to manually insert commas based on the string length. However, as you mentioned, this approach has limitations—especially when dealing with varying number lengths, multiple commas, and different value types (numbers vs currency). It can also become complex and hard to maintain as the data grows.

Given these constraints, we understand that applying a standard thousands separator is not natively supported when the field is retained as a String, and any workaround would be limited and not fully scalable.

Thanks for confirming and sharing the possible approach.

[Brett]

Hi @redyampavan,
I’ll mark this as a feature request as well to promote visibility to the support team as well.

Thank you

[redyampavan]

Hi @Brett,

Thank you for raising this as a feature request and for helping increase its visibility with the support team.

We appreciate your support and look forward to any updates from the team.

Thanks again.