Data Validation Skill

Pydantic v2 validation uses models with field and model validators to enforce data contracts on incoming requests. Define a Pydantic model matching your API schema, add custom validators for business logic (email format, password strength), and apply it to request handlers to automatically reject invalid payloads with structured error messages.

Schema alignment uses the same Pydantic models for both API validation and database operations. SQLModel extends Pydantic to create models that validate data before database writes, ensuring your API contracts and database schemas stay synchronized across multi-tenant PostgreSQL deployments.

Multi-tenant isolation in validation uses tenant_id field checks within Pydantic validators to ensure each request only accesses its own tenant's data. Add tenant_id to your model, validate it matches the requesting user's tenant, and apply this check before any database or API operation.

Pydantic v2 generates structured error messages that map field names to validation failures. Use cross-field validators for complex rules, return these errors as JSON responses with field paths, and log them for data quality monitoring to catch systematic validation issues before they propagate.

Pydantic v2 provides field validators for single-field rules and model validators for cross-field checks. You can build custom validators for password complexity, age requirements, email verification, or any domain rule, then combine them with built-in validators for comprehensive data quality enforcement.

Data quality monitoring integrates with validation by tracking validation failures and patterns over time. Log structured error data from Pydantic validators, analyze failure rates by field or tenant, and use these insights to identify schema misalignments or systematic data entry issues in your systems.