pre-dev-data-model

To model data entities before choosing a database, define a technology-agnostic structure with entity definitions, attributes, cardinality, and lifecycle policies. This ensures clear ownership and prevents schema conflicts before storage selection.

Technology-agnostic data modeling is defining data entities, relationships, and ownership rules without relying on specific SQL implementations or ORM code. It keeps conceptual structures free from product names to prevent costly migrations.

To assign data ownership for a new feature, map owning components to defined entities during the conceptual modeling phase. This assigns clear responsibility for persistent state before any database schema is implemented.

You need a Gate 5 validation checklist for data models when developing large-track features with persistent state. It validates completeness, access patterns, and confidence scoring for go/no-go decisions after API design approval.

Data modeling works without SQL or ORM dependencies by applying strict abstraction rules and DO/NEVER lists. This enforces a technology-agnostic workflow that defines relationships and lifecycle policies independently of storage products.

You should define data lifecycle policies before database selection to avoid inconsistent structures and costly migrations. Establishing access patterns and ownership early ensures schema implementations align with long-term state requirements.