db-comparison

name	db-comparison
description	Database selection — strengths and use-case mapping by type.

Relational

Database	Strengths	Watch out for
PostgreSQL	Full SQL, JSONB, extensions, ACID	Config tuning needed for performance
MySQL / MariaDB	Wide support, simple ops	Less feature-rich than Postgres
SQL Server	Enterprise features, .NET integration	Licensing cost
SQLite	Zero config, embedded	Not for concurrent writes

Document

Database	Strengths	Watch out for
MongoDB	Flexible schema, horizontal scale	No joins, eventual consistency tradeoffs
Firestore	Real-time, serverless-friendly	Query limitations, cost at scale
Cosmos DB	Multi-model, global distribution	Complex pricing, learning curve

Key-Value / Cache

Database	Use case
Redis	Cache, sessions, queues, pub/sub, leaderboards
Memcached	Simple distributed cache
ElastiCache / Memorystore / Azure Cache	Managed Redis/Memcached

Column-Family

Database	Use case
Cassandra / Keyspaces	High-write, time-series, distributed
Bigtable	Analytics, IoT, wide-column

Vector / AI

Database	Use case
pgvector (PostgreSQL ext.)	Semantic search, embeddings, RAG pipelines — load skills/architecture/llm-integration/SKILL.md
Dedicated vector DBs	Pinecone, Weaviate, Qdrant — when Postgres can't scale the vector workload

Managed Cloud (defer to cloud specialists when infra decisions needed)

• AWS: RDS, Aurora, DynamoDB, ElastiCache, DocumentDB, Redshift
• GCP: Cloud SQL, Spanner, Firestore, Bigtable, Memorystore, BigQuery
• Azure: Azure SQL, Cosmos DB, Cache for Redis, Synapse Analytics