Validation errors

When uploading or overriding data, it is essential that the data conform to the platform's schema. The platform performs validation on your data to ensure it meets the required format, types, and constraints. Due to the complexity of data compliance with our schemas, it is common to encounter validation errors during upload.

These validation errors are useful for identifying which rows or columns of your data do not conform to expectations, enabling you to make the necessary corrections and resubmit your data.

Interpreting Validation Errors

If the status of the override request is error, the validationErrors field provides detailed information about what went wrong during the validation process. The structure is designed to help you quickly identify and fix issues in your data file. 🧐

Let's look at an example of an error response:

{
    "id": "11111111-1111-1111-1111-11111111",
    "collectionId": "11111111-1111-1111-1111-11111111",
    "dataType": "contacts",
    "separator": ",",
    "dumpDatetime": "2025-08-05T15:24:50.222706Z",
    "status": "error",
    "message": "Validation failed",
    "validationErrors": [
        {
            "SCHEMA": {
                "SERIES_CONTAINS_NULLS": [
                    {
                        "schema": "ContactsDataModel",
                        "column": "Email",
                        "check": "not_nullable",
                        "error": "non-nullable series 'Email' contains null values:0    NaNName: Email, dtype: object"
                    }
                ]
            }
        }
    ]
}

Here's a breakdown of the validationErrors structure:

• validationErrors: This is a list containing the validation error objects.
• DATA: This key indicates the validation errors are related to checks performed on the data (e.g., format, type, content).
• DATAFRAME_CHECK: A specific subcategory of DATA errors, typically involving element-wise or row-wise custom checks.
• SCHEMA: This key indicates that the error is related to schema-level validation failures, such as nullability or type checks.
• SERIES_CONTAINS_NULLS: This is a specific type of schema error. In this case, it means a column that shouldn't contain null values does.
• Inside each list (e.g., DATAFRAME_CHECK, SERIES_CONTAINS_NULLS), you'll find one or more objects, each detailing a specific instance of the error:
  • schema: The name of the data schema used for validation (e.g., ContactsDataModel).
  • column: The column in your CSV file that caused the error (e.g., Email).
  • check: The specific validation rule or function that failed.
  • error: A detailed message explaining the problem, often including sample failure cases.

By examining this structure, you can pinpoint the exact column and issue in your data, correct it, and resubmit the override request. In this specific case for example the Email column in the row with index 0 contains a null value, which violates the not_nullable constraint of the data model.

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