How do you manage schema evaluation in a data system?

Schema evaluation is the process of validating that a data schema meets business rules and quality standards before it is used in downstream processes. It typically involves automated checks such as field presence, data type correctness, cardinality constraints, and referential integrity. Tools like Great Expectations, dbt's schema tests, or custom scripts can be used to run these checks against a sample dataset. The results are stored in a metadata catalog, allowing teams to track schema health over time. If a schema fails evaluation, the pipeline can block downstream jobs or trigger a remediation workflow. This ensures that only compliant data flows through the system, reducing downstream errors and improving trust in the data.