In the Sources page of your UI, the “Logs” tab provides a centralized view of any errors that occurred during the data ingestion process:

Errors typically occur when there is a schema mismatch:

  • Field Type Mismatch: The data type of a field does not match the expected type (e.g., a String instead of Integer)
  • Missing Required Fields: A required field is absent in the data being ingested
  • Unexpected Fields: The data contains fields that are not defined in the schema

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Example

For example, we can take this sample dataset of customer information:

{"name": "John Doe", "age": 36, "email": "john.doe@test.com"}
{"name": "Jane Smith", "age": 59, "email": "jane.smith@example.com"}
{"name": "Emily Johnson", "age": 56, "email": "emily.johnson@demo.net"}
{"name": "Michael Brown", "age": 52, "email": "michael.brown@test.com"}
{"name": "Sarah Davis", "age": 27, "email": "sarah.davis@mail.com"}
...
{"name": "Harper Baker", "age": 50, "email": "harper.baker@demo.net"}
{"name": "Noah Nelson", "age": 19, "email": "noah.nelson@test.com"}
{"name": "Ella Carter", "age": 47, "email": "ella.carter@example.com"}
{"name": "William Perez", "age": "thirty", "email": "william.perez@test.com"}

With the following schema:

name: String
age: Integer
email: String

Note that the final data entry has “thirty” for age, which expects an Integer.

On the “Logs” tab, we can see detailed information about the ingestion error that occured:

timestamp (Datetime): The exact date and time when the error occurred

error (String): The type of the error encountered

We can see here that the error occured at row 30 specifically for the age value.

data (String): The specific data payload that caused the error

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