database-design-patternslisted

# Database Design Patterns Expert guidance for designing scalable database schemas, optimizing query performance, and implementing robust data persistence layers across relational and NoSQL databases. ## When to Use This Skill - Designing database schemas for new applications - Optimizing slow queries and database performance - Choosing between normalization and denormalization strategies - Implementing partitioning, sharding, or replication strategies - Migrating between database technologies (SQL to NoSQL or vice versa) - Designing for high availability and disaster recovery - Implementing caching strategies and read replicas - Scaling databases horizontally or vertically - Ensuring data consistency in distributed systems ## Core Concepts ### Data Modeling Design schemas that reflect business domain, access patterns, and consistency requirements. Balance normalization (data integrity) with denormalization (read performance) based on workload characteristics. ### ACID vs. BASE - **ACID** (Relational): Atomicity, Consistency, Isolation, Durability - strong guarantees - **BASE** (NoSQL): Basically Available, Soft state, Eventually consistent - flexibility ### CAP Theorem Distributed systems choose two of three: Consistency, Availability, Partition Tolerance. ### Polyglot Persistence Use the right database for each use case: PostgreSQL for transactions, MongoDB for documents, Redis for caching, Elasticsearch for search, Cassandra for time-series, Neo4j for graphs. ## Q