data-modeling

Database schema design defines primary keys, uniqueness, cardinality, and rule-enforcing structures so stored data matches expectations and prevents invalid states. This skill specifies logical and physical structures to enforce identity and relationships.

Choose normalization versus denormalization based on write and read patterns alongside consistency tradeoffs. Schema design requires explicit tradeoffs between these approaches to shape persisted records and derived artifacts effectively.

Data provenance is captured in a physical schema by defining source-of-truth rules for derived fields and specifying how derived data refreshes. Schema design also maps long-lived obligations like retention and privacy.

Yes, this schema design translates validated conceptual models into physical structures across relational, document, graph, and event warehouse systems. It specifies entities, identity, keys, and constraints for each system type.

The best way to prepare for database migration is to design a complete physical schema with indexing and query-awareness. This skill provides handoff readiness by specifying entities, constraints, and relationships.

Indexing and query awareness are needed during data structure design to ensure logical schemas translate to performant physical implementations. Schema design requires explicit indexing strategies to support anticipated read patterns.