coding

To enforce DTO-driven development in Python components, define Pydantic schemas for data exchange and implement explicit interfaces using the eight generic protocols. This approach separates data shape from processing logic, ensuring clean architecture and strict type safety across modules.

The best way to structure Python domain logic and data pipelines is to organize code into dedicated directories like schemas/, interface/, domain/, and pipelines/. This enforces the Single Responsibility Principle and creates consistent architectural boundaries for processors and transformers.

Schema-first design with Pydantic DTOs works by defining strict data models with field validation before writing processing logic. These DTOs act as explicit data exchange contracts for DataIO operations, ensuring that only validated data enters or exits your domain logic components.

Yes, you can use explicit protocols for both transformers and processors in Python. By defining matching IProcessor and ITransformer implementations that follow the eight-protocol pattern, you establish clear input-output contracts that standardize data flow across different component types.

You should apply the Single Responsibility Principle to Python models whenever designing domain logic or data pipelines to prevent components from handling unrelated tasks. Separating concerns into distinct processors and data I/O modules ensures components remain maintainable and testable.

The Python architecture pattern provides schema-first guidance with explicit inheritance constraints. By restricting arbitrary class hierarchies in favor of composable Pydantic DTOs and protocol contracts, it prevents deep coupling and maintains predictable data exchange boundaries.