DynamoDB vs Redis: Persistent NoSQL vs In-Memory Data Store
Compare DynamoDB and Redis to understand when to use a durable NoSQL database versus an in-memory data store for caching, sessions, and real-time data.
DynamoDB vs Redis at a Glance
| Category | AWS DynamoDB | Redis |
|---|---|---|
| Storage | Durable SSD-backed (unlimited) | In-memory (limited by RAM) |
| Latency | Single-digit milliseconds | Sub-millisecond |
| Data Model | Key-value / document | Key-value with rich data structures |
| Persistence | Fully durable with backups and PITR | Optional (RDB/AOF), primarily in-memory |
| Scaling | Automatic horizontal scaling | Redis Cluster or ElastiCache |
| Querying | PartiQL, scan/query with filters and GSIs | Key-based lookups, sorted set range queries |
| Pricing | Pay-per-request or provisioned | Instance-based (ElastiCache) or per-command (Upstash) |
| Use Case | Primary database | Cache / session store / real-time layer |
DynamoDB Strengths
- Durable persistence with automatic backups and PITR
- Unlimited storage capacity
- Built-in replication and high availability
- Pay-per-request pricing for cost efficiency
- Global tables for multi-region data
- Rich querying with PartiQL, GSIs, and filter expressions
Redis Strengths
- Sub-millisecond latency (in-memory)
- Rich data structures (lists, sets, sorted sets, streams)
- Pub/Sub messaging built in
- Lua scripting for atomic operations
- Ideal for caching, sessions, and leaderboards
- Simple key-value API
When to Choose What
Choose DynamoDB when
Primary data store for applications needing durable persistence, flexible querying, and automatic scaling without managing infrastructure.
Choose Redis when
Caching layers, session stores, real-time leaderboards, rate limiting, message queues, and any use case requiring sub-millisecond access to ephemeral data.
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