# DB Nosql Modeling

> Design NoSQL data models — document, key-value, wide-column, graph, time-series — with access pattern analysis, consistency requirements, and schema evolution.

- Skill: `quantumquirkxyz/db-nosql-modeling` (Agent Skill)
- Install (CLI): `npx skillmds@latest add quantumquirkxyz/db-nosql-modeling`
- Raw SKILL.md: https://api.skillmd.com/api/skills/quantumquirkxyz/db-nosql-modeling/raw
- Safety review: pending
- Works with: Claude Code, Claude.ai, OpenAI Codex
- Category: Coding & Dev Tools
- Author: quantumquirkxyz (https://skillmd.com/u/quantumquirkxyz)
- Updated: 2026-09-17
- Page: https://skillmd.com/skills/quantumquirkxyz/db-nosql-modeling

---


## Contract

- **Input:** problem description and inputs defined by the skill body.
- **Output:** Markdown artifact with completed process steps.
- **Side effects:** none.
- **Dependencies:** none.
- **Stop condition:** all process steps executed; artifact saved with required sections.
- **Risk:** low.
- **Boundary:** produces reasoning artifact only; no system changes.


# NoSQL Data Modeling

Design a **NoSQL data model** — document, key-value, wide-column, graph, time-series — with access pattern analysis and consistency trade-offs.

## When to use

- A domain naturally fits a NoSQL store (events, social graph, time-series, cache).
- Relational schema has been over-normalised for the read/write pattern.
- Scalability requirements push past a single RDBMS instance.

## Process

1. Identify access patterns — reads (by key, range, full-text, graph traversal) and writes (append, upsert, batch); frequency and latency requirements.
2. Choose NoSQL type:
   - Document (MongoDB, Couchbase): nested JSON, flexible schema.
   - Key-value (Redis, DynamoDB): simple get/put by key.
   - Wide-column (Cassandra, ScyllaDB): time-series, high-write throughput.
   - Graph (Neo4j, DynamoDB GSI): many-to-many, traversals.
   - Time-series (InfluxDB, TimescaleDB): metric ingestion, downsampling.
3. Design data layout — document structure, key design (partition + sort), column families, graph topology.
4. Consistency model — strong vs eventual; how does this affect correctness of reads?
5. Schema evolution — how are new fields / relationships added without downtime?
6. Scalability — partition/shard key; replication factor; read replicas;