Amazon QuickSight – Unsupported Data Values

Amazon QuickSight, the cloud-based business intelligence tool, supports various data types and formats for data analysis and visualization. However, some unsupported data values may occur, and QuickSight provides options to handle such situations.

Unsupported data values in QuickSight can include null values, invalid or incorrect data types, or data outside the expected range or format. When such unsupported data values are encountered, QuickSight provides several options for handling them.

One option is to exclude or filter out the unsupported data values from the analysis and visualization. Users can use QuickSight’s filtering capabilities to exclude specific data points or exclude data that falls outside of a particular range or format.

Another option is to replace the unsupported data values with a default or custom value. For example, null values can be replaced with a default value of zero, or invalid data types can be replaced with a custom value that represents the data more accurately.

If The rows containing values that don’t conform to the data type assigned to the field by Amazon QuickSight are skipped. Consider the following source data, for example.

Sales Date is interpreted by Amazon QuickSight as a date field and dropped if it contains a nondate value, so only the following rows are imported.

In some cases, a database field contains values that the JDBC driver cannot understand. In situations where data values cannot be interpreted, they are substituted with null values to facilitate importing of the rows. This problem is only known to occur with MySQL date, datetime, and timestamp fields that have all-zero values, such as 0000-00-00 00:00:00. To illustrate, consider the following sample data.

In this case, the following data is imported.