Other Databases — Concept (Neptune, DocumentDB, Keyspaces, QLDB, Timestream, MemoryDB)
These are AWS's purpose-built databases. SAA-C03 rarely deep-dives but tests picking the right one.
Neptune
- Managed graph database.
- Two query languages: Gremlin (property graph) + SPARQL (RDF).
- Use cases: social networks, recommendation engines, fraud detection, knowledge graphs.
- Multi-AZ, up to 15 read replicas.
- Neptune Streams for change capture.
DocumentDB
- Managed MongoDB-compatible document DB (JSON).
- Drop-in for many MongoDB apps.
- Multi-AZ, scales reads with replicas.
- Up to 15 replicas, storage auto-scales.
- Best when you need MongoDB API without running it.
Keyspaces (for Apache Cassandra)
- Managed Cassandra-compatible, wide-column NoSQL.
- Serverless capacity (on-demand) or provisioned.
- CQL (Cassandra Query Language).
- Use for time-series, IoT, large-scale logs (legacy Cassandra workloads).
QLDB (Quantum Ledger Database)
- Immutable, cryptographically verifiable ledger.
- Centralized (single trusted owner) — not blockchain.
- Use for system-of-record audits, supply chain, financial records.
- Append-only, every change cryptographically chained.
Note: AWS announced QLDB end-of-life (scheduled). Still appears in exam questions today; verify on exam day. For new builds AWS suggests Aurora PostgreSQL.
Timestream
- Managed time-series database.
- Stores recent data in-memory ("memory store"), older in cheaper "magnetic store".
- SQL-like queries with time-series functions.
- Use for IoT, DevOps metrics, industrial telemetry.
MemoryDB for Redis
- Managed Redis-compatible in-memory durable database (Multi-AZ transaction log).
- ElastiCache Redis is a cache; MemoryDB is a primary database with the same API.
- Use when you need ultra-low-latency reads AND persistence as a real DB.
Picking the right one
| Workload | DB |
|---|---|
| Relationships, graph traversals | Neptune |
| MongoDB API, JSON docs | DocumentDB |
| Cassandra workload, wide-column | Keyspaces |
| Immutable audit / ledger | QLDB (legacy) or Aurora Postgres with audit |
| IoT / metrics / time-series | Timestream |
| Redis API but durable primary DB | MemoryDB |
| Key-value, massive scale | DynamoDB |
| OLTP relational | Aurora / RDS |
| Analytics / OLAP | Redshift |
| Cache | ElastiCache |
Common exam scenarios
- "Recommendation engine traverses user-product graph" → Neptune.
- "Lift-and-shift a MongoDB workload" → DocumentDB.
- "Existing Cassandra app, want managed" → Keyspaces.
- "Immutable, cryptographically verifiable order history" → QLDB (today; verify exam currency).
- "Millions of IoT sensor readings, query last 30 days" → Timestream.
- "Redis API but as primary durable DB" → MemoryDB.
Exam tip
Pick by workload model, not by perceived performance:
- relational? → RDS / Aurora
- key-value, massive scale? → DynamoDB
- graph? → Neptune
- doc (MongoDB)? → DocumentDB
- wide-column (Cassandra)? → Keyspaces
- time-series? → Timestream
- ledger? → QLDB (legacy)
- in-memory cache? → ElastiCache
- in-memory DB (durable)? → MemoryDB.