AWS Databases Types & Choosing the Right Option | Overview

AWS Databases Types & Choosing the Right Option - Overview.

Scope:

• Intro,
• Types of Databases in AWS,
• Relational Databases (SQL),
• NoSQL Databases (Key-Value, Document, Wide Column),
• In-Memory Databases,
• Graph Databases,
• Ledger Databases,
• Analytics/OLAP Databases,
• Table for choosing the right database for workload,
• Quick Cheat Sheet.

Intro:

• AWS offers a wide array of purpose-built databases, each optimized for specific data models and application requirements. 
• Choosing the right option depends on twtech data's structure, performance needs, query patterns, and scalability requirements. 

1. Types of Databases in AWS

NB:

• AWS databases are broadly categorized into:
• Relational, 
• NoSQL, 
• In-memory, 
• Graph, 
• Ledger, 
• Analytics/OLAP. 

Here's a breakdown:

A. Relational Databases (SQL)

• Purpose: Structured data with relationships, ACID transactions.
• AWS Services:
• Amazon RDS – Managed relational databases (MySQL, PostgreSQL, MariaDB, Oracle, SQL Server).
• Amazon Aurora – High-performance, fully managed MySQL/PostgreSQL-compatible database.
• Use Cases:
• E-commerce transactions
• Financial systems
• Applications requiring complex joins and constraints
• Pros: ACID compliance, mature SQL ecosystem
• Cons: Scaling writes horizontally is harder (Aurora can scale better)

B. NoSQL Databases (Key-Value, Document, Wide Column)

• Purpose: Unstructured/semi-structured data, high throughput, low latency.
• AWS Services:
• Amazon DynamoDB – Key-value and document store; fully managed, serverless.
• Amazon Keyspaces – Managed Apache Cassandra-compatible wide-column store.
• Use Cases:
• Session management
• Gaming leaderboards
• IoT data
• Event-driven architectures
• Pros: Scales horizontally, low-latency, flexible schema
• Cons: Limited relational queries (joins), eventual consistency in some cases

C. In-Memory Databases

• Purpose: Ultra-low latency, caching, transient or frequently accessed data.
• AWS Services:
• Amazon ElastiCache (Redis or Memcached)
• Use Cases:
• Caching web sessions
• Real-time analytics
• Leaderboards
• Pros: Extremely fast reads/writes
• Cons: Data is usually volatile (though Redis supports persistence)

D. Graph Databases

• Purpose: Relationships and connections between entities.
• AWS Services:
• Amazon Neptune – Fully managed graph DB (supports Property Graph and RDF/SPARQL).
• Use Cases:
• Social networks
• Fraud detection
• Recommendation engines
• Pros: Efficient traversal of relationships
• Cons: Not ideal for general-purpose structured or document data

E. Ledger Databases

• Purpose: Immutable, cryptographically verifiable transaction logs.
• AWS Services:
• Amazon QLDB – Fully managed ledger DB.
• Use Cases:
• Financial transactions
• Supply chain tracking
• Auditing systems
• Pros: Integrity and verifiability of data
• Cons: Specialized use cases

F. Analytics/OLAP Databases

• Purpose: Complex analytics, aggregations over large datasets.
• AWS Services:
• Amazon Redshift – Columnar data warehouse, high-performance analytics.
• Amazon Athena – Query data in S3 using SQL, serverless.
• Amazon Timestream – Time-series database for IoT and metrics.
• Use Cases:
• Data warehouses
• IoT telemetry analysis
• Business intelligence dashboards
• Pros: Optimized for large-scale analytics
• Cons: Not ideal for transactional workloads

2. Table for Choosing the Right Database 

Consider these factors:

Factor	Questions to Ask	Examples
Data Structure	Is it relational, key-value, document, graph?	RDS for structured, DynamoDB for key-value
Query Patterns	Complex joins or simple key lookups?	Aurora for joins, DynamoDB for simple lookups
Scale & Performance	Need horizontal scale, low latency?	DynamoDB, ElastiCache
Transactions	Require ACID guarantees?	Aurora, RDS
Analytics	Batch vs real-time analytics?	Redshift for batch, Timestream for time-series
Cost & Management	Fully managed vs self-managed	RDS and DynamoDB are fully managed
Access Pattern	Random reads/writes vs sequential writes	DynamoDB for random, Timestream for sequential

Quick Cheat Sheet

Workload	Best AWS Database
Online transactions with complex relationships	RDS / Aurora
Massive scale key-value or document	DynamoDB
Ultra-fast caching	ElastiCache (Redis)
Graph relationships	Neptune
Immutable, verifiable ledger	QLDB
Big data analytics	Redshift / Athena / Timestream