data-validation

You can validate CSV and JSON files by inspecting them for column mismatches, duplicate records, and missing fields. The process checks record integrity and structural consistency across files and application boundaries.

The best way to validate API payloads is using Zod for TypeScript and Pydantic for Python. These frameworks enforce strict typing, handle nullable and optional fields, and validate computed models for type-safe application boundaries.

Yes, you can create and apply JSON Schema rules to verify data structure, types, and required fields. This approach enforces trustworthy shapes for inputs, outputs, and stored records across APIs and files.

To check data integrity during migrations, compare source and target exports to detect missing records, confirm field values were preserved, and identify migration drift. This includes consistency checks for duplicates, counts, and structural mismatches.

Yes, Pydantic validation supports strict typing, discriminated unions, nullable fields, and optional field handling. This ensures type-safe validation for API payloads, user inputs, and computed models across application boundaries.

Data validation fails across application boundaries when strict typing, nullable fields, and optional field handling are not consistently enforced. Applying schema rules with frameworks like Pydantic or Zod resolves these structural mismatches.