ddia-systems

SKILL.md

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 complexity dominate.

Report the current score, which diagnostic rows failed, and the improvements needed to reach 10/10.

The DDIA Framework