Data cleaning rules


A validation is a pre-determined rule to which data must conform. It determines how data should be cleaned. Validations are applied to data to ensure the data meets specific requirements (business logic). A validation passes when the data complies with the rule, and fails when the data is in a form that is not acceptable.

Each cell in a column with a validation will be checked individually against the specified rules.

Native validations

OneSchema has a library of native data validations. These are no-code validations that can be applied with as little as one click. Examples include Number to ensure the data imported only contains numbers and Email which checks a string for valid email syntax.

A list of OneSchemas data validation library can be found here.

Validation Webhook

A validation webhook is a way to use an API endpoint to create a custom validator. OneSchema will pass data to a specified endpoint, and it can be validated against a database, a third party data source, or complex logic checks can be performed. OneSchema simply expects a response of errors and warnings.

See our API documentation on Validation Webhooks here.



A number validator specifies that the data contained in each cell of a column must be an integer between -5 and 24. The validator does not allow commas.

See the following data...

3.7Fail, non-integer
-6Fail, out of range
qwFail, not a number
7,5Fail, comma not permitted


An email validator ensures that each string is in valid email format.

See the following data...

[email protected]Pass
hello@@oneschema.coFail, double @
hello@oneschemacoFail, missing .
hellooneschema.coFail, missing @
email me hereFail... in many ways