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• Common Errors & Troubleshooting
At Ekyam, we troubleshoot the errors of Authentication Failure, Data Mapping as mentioned below:

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Authentication Failure

  • Error Message Example: 401 Unauthorized or Authentication Failed
  • Explanation: A 401 Unauthorized error means that the system attempted to access an external service or API, but the credentials provided (e.g., API key, OAuth token) were either missing, invalid, or expired. The service denied access because it could not verify the identity or authorization of the request.
Troubleshooting Steps
  1. Check API Keys/Credentials: Verify that the API keys, client IDs, client secrets, or other authentication credentials configured for the connector are correct and match what is expected by the external service.
  2. Refresh Tokens: If using OAuth, the access token might have expired. Initiate the OAuth re-authentication process to obtain a new, valid token.
  3. Permissions: Ensure the authenticated user or application has the necessary permissions to perform the requested operations on the external system.
  4. Network/Firewall: In rare cases, firewall rules might be blocking the authentication handshake.

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Data Mapping Error

  • Error Message Example: Missing Required Field: ‘product_name’, Invalid Data Type for ‘price’, Schema Mismatch.
Explanation: A “Missing Required Field’ error occurs when the data does not match to the expected schema or requirements of a workflow step. This primarily implies that a mandatory field is absent or the data is an incorrect format.

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Troubleshooting Steps

→ Review Mapping Configuration: Navigate back to the data mapping configuration for the affected connector or workflow step. →Identify Missing Fields : Compare the fields present in your source data with the required fields in the destination schema. Ensure all mandatory fields are correctly mapped. → Check Data Types: Verify that the data types of the mapped fields match the expected data types in the destination. For example, ensure a numerical field is not being sent as a string. → Transformations: If data needs to be transformed (e.g., concatenate fields, format dates), ensure the transformation logic is correctly applied before the data reaches the problematic step. → Source Data Validation: Examine the source data itself to ensure it contains the expected fields and formats.
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