How do you manage schema-driven validation in pipelines?

• A. Validate only at the end.
• B. Skip checks for performance.
• C. Enforce schema checks at ingestion and transformation steps; use contracts to validate types and constraints.
• D. Use manual review only.

Answer: (C)

Schema-driven validation is most effective when you enforce data contracts early and throughout the pipeline. In Palantir Foundry, you define contracts that specify expected data types, nullability, allowed values, and other constraints. Checking these at ingestion ensures raw data conforms to the agreed schema before it flows further, so bad records are detected fast and don’t pollute downstream processes. Then, applying the same or additional contracts during transformations verifies that derived data still matches the target schema, catching issues from processing or data drift. This approach provides quick feedback, maintains data quality, and supports governance by clearly enforcing data shapes. Relying on end-only validation, skipping checks for performance, or relying on manual review can allow bad data to slip through or become unmanageable at scale.