Changing a field data type - Amazon QuickSight

Changing a field data type

When Amazon QuickSight retrieves data, it assigns each field a data type based on the data in the field. The possible data types are as follows:

  • Date – The date data type is used for date data in a supported format. For information about the date formats Amazon QuickSight supports, see Data source quotas.

  • Decimal – The decimal data type is used for numeric data that requires one or more decimal places of precision, for example 18.23. The decimal data type supports values with up to four decimal places to the right of the decimal point. Values that have a higher scale than this are truncated to the fourth decimal place in two cases. One is when these values are displayed in data preparation or analyses, and one is when these values are imported into QuickSight. For example, 13.00049 is truncated to 13.0004.

  • Geospatial – The geospatial data type is used for geospatial data, for example longitude and latitude, or cities and countries.

  • Integer – The int data type is used for numeric data that only contains integers, for example 39.

  • String – The string data type is used for nondate alphanumeric data.

QuickSight reads a small sample of rows in the column to determine the data type. The data type that occurs most in the small sample size is the suggested type. In some cases, there might be blank values (treated as strings by QuickSight) in a column that contains mostly numbers. In these cases, it might be that the String data type is the most frequent type in the sample set of rows. You can manually modify the data type of the column to make it integer. Use the following procedures to learn how.

Changing a field data type during data prep

During data preparation, you can change the data type of any field from the data source. On the Change data type menu, you can change calculated fields that don't include aggregations to geospatial types. You can make other changes to the data type of a calculated field by modifying its expression directly. Amazon QuickSight converts the field data according to the data type that you choose. Rows that contain data that is incompatible with that data type are skipped. For example, suppose that you convert the following field from String to Integer.

10020 36803 14267a 98457 78216b

All records containing alphabetic characters in that field are skipped, as shown following.

10020 36803 98457

If you have a database dataset with fields whose data types aren't supported by Amazon QuickSight, use a SQL query during data preparation. Then use CAST or CONVERT commands (depending on what is supported by the source database) to change the field data types. For more information about adding a SQL query during data preparation, see Using SQL to customize data. For more information about how different source data types are interpreted by Amazon QuickSight, see Supported data types from other data sources.

You might have numeric fields that act as dimensions rather than metrics, for example ZIP codes and most ID numbers. In these cases, it's helpful to give them a string data type during data preparation. Doing this lets Amazon QuickSight understand that they are not useful for performing mathematical calculations and can only be aggregated with the Count function. For more information about how Amazon QuickSight uses dimensions and measures, see Setting fields as a dimensions or measures.

In SPICE, numbers converted from numeric into an integer are truncated by default. If you want to round your numbers instead, you can create a calculated field using the round function. To see whether numbers are rounded or truncated before they are ingested into SPICE, check your database engine.

To change a field data type during data prep
  1. From the QuickSight start page, choose Datasets, choose the dataset that you want, and then choose Edit dataset.

  2. In the data preview pane, choose the data type icon under the field you want to change.

  3. Choose the target data type. Only data types other than the one currently in use are listed.

Changing a field data type in an analysis

You can use the Field list pane, visual field wells, or on-visual editors to change numeric field data types within the context of an analysis. Numeric fields default to displaying as numbers, but you can choose to have them display as currency or as a percentage instead. You can't change the data types for string or date fields.

Changing a field's data type in an analysis changes it for all visuals in the analysis that use that dataset. However, it doesn't change it in the dataset itself.


If you are working in a pivot table visual, applying a table calculation changes the data type of the cell values in some cases. This type of change occurs if the data type doesn't make sense with the applied calculation.

For example, suppose that you apply the Rank function to a numeric field that you modified to use a currency data type. In this case, the cell values display as numbers rather than currency. Similarly, if you apply the Percent difference function instead, the cell values display as percentages rather than currency.

To change a field's data type
  1. Choose one of the following options:

    • In the Field list pane, hover over the numeric field that you want to change. Then choose the selector icon to the right of the field name.

    • On any visual that contains an on-visual editor associated with the numeric field that you want to change, choose that on-visual editor.

    • Expand the Field wells pane, and then choose the field well associated with the numeric field that you want to change.

  2. Choose Show as, and then choose Number, Currency, or Percent.