simple-data-patterns

Async CRUD persistence for dataclass domain entities is implemented using the repository pattern with asynchronous methods mapped to a SQL backend. This approach separates pure business data from Pydantic API boundary schemas for straightforward storage operations.

Mapping Pydantic models to a SQL storage layer without complex architectures is achieved by using direct Pydantic schemas for API boundaries and dataclass domain entities for business data. The repository pattern handles standard CRUD operations without medallion architectures.

The repository pattern for simple data models is needed when applications require straightforward CRUD persistence without the overhead of raw data stores. It suits internal settings storage, prototyping product catalogs, or caching API responses where data can be re-pulled.

Yes, this CRUD pattern supports upserting external API data into SQL backends using sync patterns designed for external data synchronization. It maps Pydantic schemas and dataclass entities directly to storage, allowing cached API responses to be updated efficiently.

Yes, you can use Pydantic schemas for API boundaries while keeping domain logic in dataclasses. This pattern separates pure business data in dataclass domain entities from API validation schemas, mapping both directly to an async SQL storage layer.

Yes, SQL migration templates are included for creating tables in the repository pattern. These templates provide easy table creation for storing dataclass domain entities mapped through Pydantic schemas in straightforward CRUD applications.