ddia-systems

# Designing Data-Intensive Applications Framework A principled approach to building reliable, scalable, and maintainable data systems. Apply these principles when choosing databases, designing schemas, architecting distributed systems, or reasoning about consistency and fault tolerance. ## Core Principle **Data outlives code.** Applications are rewritten and frameworks come and go, but data persists for decades -- prioritize the long-term correctness, durability, and evolvability of the data layer. Most applications are data-intensive, not compute-intensive: the hard problems are data volume, complexity, and rate of change, and explicit consistency/availability/latency trade-offs separate robust systems from fragile ones. ## Scoring **Goal: 10/10.** Score a data architecture by the seven Quick Diagnostic rows below: award ~1.4 points per row answered "yes" with evidence (deliberate, documented trade-off), 0 where the answer is "no" or unknown. - **9-10:** every domain choice -- data model, storage engine, replication, partitioning, isolation, derived-data, fault handling -- is deliberate, documented, and matched to actual read/write/consistency requirements; failover tested. - **5-6:** core choices made but two or three diagnostic rows fail -- e.g. default isolation level unknown, hot-key risk unhandled, or failover untested. - **<=3:** choices driven by familiarity, not requirements; ignored failure modes (replication lag, write skew, hot partitions) and accidental com...