05-schema-validation

Prevent schema mismatch errors by performing runtime schema validation that compares DataFrame columns against target DDL schemas before executing Delta Lake merge operations, catching column discrepancies before deployment.

Gold layer merge scripts fail with UNRESOLVED_COLUMN errors when DataFrame columns do not align with the target table DDL schema, requiring runtime validation to detect extraneous or missing columns before deployment.

Validate a DataFrame against a DDL schema by using helper functions to enforce the live table schema as the source of truth, automatically verifying that input columns match required DDL specifications before merge execution.

Schema drift in Gold layer tables is caused by mismatches between design specifications and actual table structures, which you can prevent by adopting a DDL-first workflow that treats the live table schema as the definitive source.

Yes, you can check for extraneous columns by running runtime schema validation that verifies input DataFrames contain only the required columns defined by the target Gold table DDL schema, preventing unexpected merge failures.

Yes, schema validation works with existing Delta Lake merge scripts by applying helper functions to validate DataFrame columns against DDL schemas at runtime, ensuring data integrity without requiring design specification changes.