principle-data-modeling

What problem does it solve?

It prevents painful rework by guiding you to design schemas that match real access patterns, so your database stays fast, consistent, and evolvable as the product grows.

Core Features & Use Cases

• Query-first storage and structure: choose relational vs document vs KV/wide-column vs graph based on access patterns and avoid mismatched paradigms.
• Normalization, denormalization, and indexing discipline: decide normalization depth and build indexes that support the primary read path while minimizing write amplification.
• Operational safety for change: apply expand–contract schema evolution, plan retention/TTL/archival, and address hot-key/partition skew with practical mitigations.

Quick Start

Ask the AI to design the schema for your system by listing your top read/write access patterns and requesting an indexing plan plus an expand–contract migration strategy for a schema change.