Validating Silver and Gold datasets for quality

In the medallion architecture, Silver and Gold datasets must be validated before use downstream, and Delta Lake plus Delta Live Tables (DLT) provide built-in mechanisms to enforce this. Data quality checks range from simple constraints on Delta tables to declarative expectations in DLT pipelines that can warn, drop, or fail on bad records.

1 · Learn the must-know

• 1Delta Lake supports NOT NULL and CHECK constraints on tables; violating writes are rejected outright, enforcing schema-level data quality.
• 2DLT expectations use the @dlt.expect (warn only, row kept), @dlt.expect_or_drop (row dropped), and @dlt.expect_or_fail (pipeline fails) decorators to define row-level validation rules.
• 3DLT tracks expectation metrics (records passed/failed) in the pipeline event log, giving auditable visibility into data quality over time.
• 4Schema enforcement (rejecting mismatched writes) and schema evolution (mergeSchema) are distinct Delta Lake features; enforcement protects Silver/Gold integrity while evolution must be explicitly opted into.
• 5Quarantining bad records (writing failed rows to a separate table) is a common pattern for expect_or_drop style checks so data isn't silently lost.
• 6Data quality checks are typically applied progressively: looser validation in Bronze-to-Silver, stricter business-rule validation in Silver-to-Gold, since Gold feeds reporting and ML.

2 · Check your understanding