Data Systems

Choosing the right database architecture involves evaluating your application's data access patterns to propose an optimal persistence strategy. This framework defaults to PostgreSQL while providing decision criteria for selecting alternative database paradigms based on specific workload requirements.

Caching should be used when read-heavy workloads demand lower latency and reduced database load. The framework provides decision criteria for implementing caching layers alongside your primary data stores to improve query performance and system scalability.

The best way to plan a database migration involves assessing schema changes, data modeling adjustments, and performance tuning across target storage systems. This framework provides structured migration planning guidance for transitioning between relational, document, and vector data stores.

Event sourcing works by storing state changes as a sequence of immutable events rather than directly mutating current state. This framework provides decision criteria for when to apply event sourcing in your data architecture for reliable real-time processing and analytics workloads.

Designing a data layer for a multi-tenant application requires selecting appropriate storage paradigms, modeling schemas for tenant isolation, and tuning query performance. This framework guides storage decisions across relational, document, and vector data stores for multi-tenant architectures.

PostgreSQL should not be used when your workload demands specialized database paradigms like document stores, vector databases, or event sourcing systems. This framework provides CAP considerations and performance-first design criteria for identifying when alternative databases outperform PostgreSQL.