From role-database
Data modeling methodologies and patterns across paradigms. Entity-Relationship modeling (Chen, Crow's Foot, UML), document modeling (embedding vs referencing, polymorphic, bucket, outlier patterns), graph modeling (labeled property graph, RDF), time-series modeling, event sourcing, dimensional modeling (star/snowflake schema, SCD), Data Vault (hubs, links, satellites), polyglot persistence. Use when designing data models, choosing between modeling approaches, or mapping domain models to database schemas.
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ER notation systems (Chen, Crow's Foot, UML, IDEF1X). Relationship types (1:1, 1:N, M:N, self-referential, polymorphic) with implementation patterns. Normalization decision guide by scenario (OLTP, writes, reads, warehouse). MongoDB embedding vs referencing decision table. Document design patterns: polymorphic, bucket (time-series), outlier, subset, computed. Load when: designing relational schemas, modeling MongoDB documents, or choosing ER notation.
Labeled property graph model syntax and best practices. RDF triple model and SPARQL use cases. Time-series design principles, TimescaleDB hypertable, PostgreSQL native range partitioning. Star schema structure, SCD types comparison table, SCD Type 2 implementation. Data Vault components (hub/link/satellite) and advantages. Polyglot persistence decision table and consistency patterns (CDC, Saga, CQRS). Load when: designing graph models, time-series schemas, data warehouses, or multi-database architectures.