Handling Exceptions

Exceptions refer to the records or data points that fail to meet predefined rules, criteria, or quality standards during the Extract, Transform, and Load (ETL) process. Exceptions are important because they identify errors that could disrupt the ETL process or compromise the quality of the target data. Handling exceptions, ensure that only accurate and clean data reaches the target system. This prevents errors that could lead to incorrect business decisions and ensures that analysis and reporting are based on reliable information.

In ETL workflows, some exceptions may pass the first layer of transformation rules but fail further validations later in the pipeline. These records might initially appear valid because they meet basic formatting or structure criteria but still contain underlying issues that violate deeper business rules, relational integrity, or target system requirements.

In ETL the initial transformation rules might only check for technical conditions, such as correct formatting or non-empty fields. They do not validate the data for business rules or key dependencies.

Example

• A loan amount field contains a valid number format but is unrealistically high or negative, which violates business logic.
• A customer ID exists in the loan data but has no corresponding record in the Dim Customer table.
• A record passes initial format checks based on the type but exceeds the character limit in the database schema.