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Utilize default validation options to build your desired data import experience. |
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- String Validator: This validator ensures that the column value is a string. String and Number are both valid values.
- Number Validator: The Number validator verifies that the column value is a valid numeric entry. It only permits numerical values like
12
but will not allow12.33
andjohn
. - Double Validator: The Double validator verifies that the column value is either a Number or a Number with decimals. Valid values are
12
and12.5
butjohn
is not valid. - Email Validator: With the Email validator, you can ensure that the column value conforms to a valid email format. Valid values look like
john@gmail.com
while values likejohn
andjohn.com
are not valid. - Regex Validator: The Regex validator enables you to define a custom regular expression pattern that the column value must match.
- Select Validator: Use the Select validator to restrict column values to a predefined set of options. For instance, you can use it to ensure that a "Gender" column contains only values like "Male" or "Female."
- Any Validator: Any validator offers maximum flexibility by allowing any value in the column.
- isRequired: Ensures that a value must exist in each cell for the column.
- isUnique: Ensures that the value is unique throughout the column.
- allowMultiSelect: Accepts multiple values separated by
,
for the cell.
It's possible to extend validation functionality to adjust according to your needs. Read more in custom-validation.md.
{% include "../.gitbook/includes/your-feedback-is-crucial-in....md" %}