data-systems-architecture

Database schema design for data-heavy apps balances normalization versus denormalization, storage efficiency, and query performance. Start with foundational principles around reliability and scalability, then apply indexing and storage strategies tailored to your access patterns and growth projections.

OLTP optimizes transactional workloads with frequent writes and small reads; OLAP optimizes analytical workloads with large batch reads. Evaluate trade-offs early in design: OLTP prioritizes consistency and concurrency, OLAP prioritizes query performance on historical data aggregates.

N+1 queries occur when an ORM fetches one parent record then issues separate queries for each related child. Address this by understanding your storage engine's join capabilities, selecting appropriate indexing strategies, and designing schemas that reduce unnecessary round trips.

Indexing strategies depend on query patterns, write frequency, and scale. Choose between B-tree, hash, and specialized index types; balance read speed gains against write overhead and storage cost. Plan index evolution as data and access patterns grow.

Scalability planning addresses storage capacity, query latency, and transaction concurrency early. Apply patterns for sharding, replication, and transaction isolation levels; choose storage engines and schema designs that sustain reliability and maintainability as volume increases.