Database - RDS,Aurora, DynamoDB, S3, documentDB, Elasticache, Neptune, KeySpace and timestream

First, understand that not every problem needs the same type of database. AWS offers many database services, each built for a specific use case.

Type	What it means	AWS Service
RDBMS / SQL	Tables, rows, relationships, joins	RDS, Aurora
NoSQL	Flexible, no fixed schema, no joins	DynamoDB, ElastiCache, DocumentDB, Neptune, Keyspaces
Object Store	Store large files/objects as-is	S3, Glacier
Data Warehouse	Analytics on huge datasets, BI reporting	Redshift, Athena, EMR
Search	Free text search, unstructured queries	OpenSearch
Graph	Data with many relationships	Neptune
Ledger	Immutable financial records	Quantum Ledger DB
Time Series	Data tied to timestamps, IoT, metrics	Timestream

Amazon RDS

Simple explanation: A fully managed relational database. You pick the engine, AWS handles everything else — backups, patching, scaling, high availability.

Supported engines: PostgreSQL, MySQL, Oracle, SQL Server, MariaDB, DB2, Custom.

Key features:

Provisioned instance size + EBS volume type and size
Auto-scaling for storage — grows automatically when needed
Read Replicas — extra copies for read traffic, reduces load on main DB
Multi-AZ — standby DB in another AZ, automatic failover if main fails
Automated backups with Point-in-Time Restore up to 35 days
Manual snapshots for longer-term backup
Security via IAM, Security Groups, KMS encryption, SSL in transit
Integrates with Secrets Manager for credential rotation
RDS Custom — for Oracle and SQL Server where you need OS/DB level access

Use case: Any relational data — transactions, SQL queries, structured data with joins (OLTP workloads).

Amazon Aurora

Simple explanation: Aurora is AWS's own high-performance take on relational databases — compatible with PostgreSQL and MySQL but much faster, more available, and more automated than standard RDS.