Overview

Type conversion errors occur when a value stored in a field does not match the current data type definition. Typical error format:

'<value>' was not recognized as a valid <DataType>

These errors are generated when the source system returns or stores values that cannot be converted into the expected type. This issue usually occurs due to a mismatch between historical data and the current field type definition in the source system.

Common Scenarios

• Field type was changed after data was already stored.
• Legacy records still contain values in the old format.
• Source system allowed inconsistent or free-text values previously.
• Data was imported without strict type validation.

All errors in this category follow the same structure: a value incompatible with the field data type, identified by Table, Column, and Id. The fix requires updating the value to a valid format.

Error Types and Resolution

1. Boolean conversion error

Error: 'undefined' was not recognized as a valid Boolean: [Table: "Table_Name", Column: "Column_Name", Id: 'xxxxxxxxxxxxxx']

Cause: The Boolean field contains an invalid value such as undefined. This typically happens when the field type was changed from string to Boolean and existing values were not updated. Although HubSpot defines this field as Boolean (allowed values: true or false), older records may still contain invalid values due to historical data inconsistencies.

Resolution: Update the field to a valid Boolean value in the HubSpot UI or via Skyvia Query:

UPDATE Table_Name
SET "Column_Name" = false
WHERE id = xxxxxxxxxxxxxx

2. DateTime conversion error

Error: 'xx/xx/xx' was not recognized as a valid DateTime: [Table: "Table_Name", Column: "Column_Name", Id: 'xxxxxxxxxxxxxx']

Cause: The field contains a value that does not match the expected DateTime format. This often happens after changing a field type from text to DateTime, while legacy values remain unchanged.

Resolution: Update the field with a valid DateTime value:

UPDATE Table_Name
SET "Column_Name" = 'yyyy-mm-dd'
WHERE id = xxxxxxxxxxxxxx

3. Double (numeric) conversion error

Error: 'xxx' was not recognized as a valid Double: [Table: "Table_Name", Column: "Column_Name", Id: 'xxxxxxxxxxxxxx']

Cause: The field contains a non-numeric value (e.g. text or formatted numbers) that cannot be converted to Double. This typically happens when numeric formatting rules changed after data was already stored.

Resolution: Update the field with a valid numeric value:

UPDATE Table_Name
SET "Column_Name" = 0
WHERE id = xxxxxxxxxxxxxx

4. Int64 conversion error

Error: '1234567.0' was not recognized as a valid Int64: [Table: "Table_Name", Column: "Column_Name", Id: 'xxxxxxxxxxxxxx']

Cause: The field contains a decimal or string value that cannot be converted to an integer (Int64). This usually happens when numeric values were changed after data was already stored.

Resolution: Update the field with a valid integer value:

UPDATE Table_Name
SET "Column_Name" = 1234567
WHERE id = xxxxxxxxxxxxxx

Notes

• Some invalid values may not be visible in the UI but still exist in the database.
• Connector validation prevents new invalid values, but historical data may still contain them.
• This is a common scenario when field types are changed in source systems.
• All errors in this category indicate the same root cause: the stored value does not match the current data type of the field. Updating the affected record with a valid value resolves the issue.