NoSQL Database Selection Guide

NoSQL isn’t a technology — it’s a category of databases that trade relational constraints for specific performance characteristics. Every NoSQL database excels at one access pattern and struggles with others. Choosing the right one means understanding your data model and access patterns before picking a database, not after.

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NoSQL Categories

Category	Data Model	Best For	Example
Document	JSON-like nested objects	Variable schemas, content, catalogs	MongoDB, DocumentDB, Firestore
Key-Value	Simple key → value	Caching, sessions, configuration	Redis, DynamoDB, Memcached
Wide-Column	Row key → columns (sparse)	Time-series, IoT, analytics	Cassandra, HBase, ScyllaDB
Graph	Nodes + edges	Social networks, recommendations	Neo4j, Neptune, ArangoDB
Time-Series	Timestamp → values	Metrics, monitoring, IoT sensors	TimescaleDB, InfluxDB, QuestDB
Vector	Embeddings + metadata	AI/ML similarity search	Pinecone, Weaviate, Milvus

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Decision Framework

What's your primary access pattern?

Complex relationships, traversals?
└── Graph database (Neo4j, Neptune)

Simple key lookups, high speed?
└── Key-Value (Redis, DynamoDB)

Flexible schema, nested documents?
└── Document store (MongoDB, Firestore)

Time-stamped data, high write volume?
└── Time-series (TimescaleDB, InfluxDB)

Wide rows, append-heavy, time-ordered?
└── Wide-column (Cassandra, ScyllaDB)

Similarity search, embeddings?
└── Vector database (Pinecone, Weaviate)

Complex queries, joins, transactions?
└── Relational database (PostgreSQL)

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When to Use NoSQL vs Relational

Need	Relational (PostgreSQL)	NoSQL
ACID transactions	✅ Native	⚠️ Limited (some support it)
Complex queries/joins	✅ Excellent	❌ Poor across collections
Schema flexibility	⚠️ Migrations needed	✅ Schema-free
Horizontal scaling	⚠️ Read replicas, Citus	✅ Built-in sharding
High write throughput	⚠️ Limited by single primary	✅ Distributed writes
Known query patterns	✅ Ad-hoc queries easy	✅ Optimized for specific patterns
Unknown query patterns	✅ SQL handles anything	❌ Must design for patterns upfront

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Data Modeling Comparison

// RELATIONAL: Normalized
// orders table → order_items table → products table
// 3 tables, joined at query time

// DOCUMENT (MongoDB): Denormalized
{
  "_id": "order-123",
  "customer": {
    "id": "cust-789",
    "name": "John Doe",
    "email": "john@example.com"
  },
  "items": [
    {
      "product_id": "prod-456",
      "name": "Widget Pro",
      "quantity": 2,
      "price": 29.99
    }
  ],
  "total": 59.98,
  "status": "shipped"
}
// 1 document, no joins, fast reads
// Trade-off: customer data duplicated across orders

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Anti-Patterns

Anti-Pattern	Problem	Fix
NoSQL for everything	Struggling with JOINs, transactions	Use relational for relational data
Relational for everything	Fighting scale, schema rigidity	NoSQL for specific high-scale patterns
MongoDB for relational data	Denormalization nightmare, data inconsistency	PostgreSQL with JSONB for flexibility
DynamoDB without understanding access patterns	Hot partitions, expensive scans	Design access patterns before choosing DynamoDB
Graph database for tabular data	Over-engineering	Graph only when traversals are the primary query

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Checklist

• Access patterns documented before selecting database
• Consistency requirements defined (strong vs eventual)
• Scale requirements: read/write throughput, data volume
• Database category selected based on actual needs
• Data model designed for primary access pattern
• Backup and recovery strategy
• Monitoring: latency, throughput, storage
• Migration plan: how to move data if needs change

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:::note[Source] This guide is derived from operational intelligence at Garnet Grid Consulting. For database consulting, visit garnetgrid.com. :::