AWS offers a broad portfolio of fully managed database services designed to eliminate the undifferentiated heavy lifting of database administration — patching, backups, replication, and failover. Rather than forcing every workload into a single database engine, AWS provides purpose-built databases for relational, NoSQL, in-memory, graph, ledger, and analytics use cases. Knowing which database type matches a given workload is a consistently tested skill in the CLF-C02 exam.Documentation Index
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Amazon RDS
Managed relational databases supporting MySQL, PostgreSQL, Oracle, SQL Server, and Aurora
Amazon DynamoDB
Serverless NoSQL key-value database delivering single-digit millisecond performance at any scale
Amazon ElastiCache
In-memory caching with Redis and Memcached for microsecond response times
Amazon Redshift
Managed petabyte-scale data warehouse for analytics and business intelligence
AWS Database Services in Depth
Amazon RDS (Relational Database Service)
Amazon RDS (Relational Database Service)
Amazon RDS is a fully managed service that makes it easy to set up, operate, and scale relational databases in the cloud. AWS handles time-consuming tasks including hardware provisioning, database setup, patching, and automated backups — so you can focus on your applications.
Supported Database Engines
- Amazon Aurora (AWS-native, covered separately below)
- MySQL
- PostgreSQL
- MariaDB
- Oracle Database
- Microsoft SQL Server
Key Features
| Feature | Description |
|---|---|
| Automated Backups | Daily automated backups with point-in-time recovery (up to 35 days retention) |
| Multi-AZ Deployment | Synchronous standby replica in a different AZ for high availability and automatic failover |
| Read Replicas | Asynchronous copies to offload read traffic and improve read scalability (up to 15 replicas for Aurora) |
| Automated Patching | AWS handles OS and database engine patch management |
| Encryption | At-rest (AES-256) and in-transit (SSL/TLS) encryption |
What You Do NOT Manage with RDS
Operating system installation, database engine installation, hardware provisioning, and most patching tasks are handled by AWS. This is the key distinction between RDS (managed) and running a database on a self-managed EC2 instance.Multi-AZ vs Read Replicas
- Multi-AZ — For high availability and disaster recovery. The standby does not serve read traffic; it exists only for failover.
- Read Replicas — For read scalability. They serve read traffic and can be promoted to standalone databases.
Amazon Aurora
Amazon Aurora
Amazon Aurora is an AWS-built relational database engine that is fully compatible with MySQL and PostgreSQL while delivering significantly higher performance and availability than standard community editions.
Performance Advantages
- Up to 5x faster than standard MySQL
- Up to 3x faster than standard PostgreSQL
- Distributed, fault-tolerant storage that automatically replicates data across 3 Availability Zones with 6 copies of your data
Key Features
- Up to 15 read replicas with sub-10ms replica lag
- Aurora Serverless v2 — automatically scales compute capacity up and down based on application demand (ideal for unpredictable workloads)
- Aurora Global Database — spans multiple AWS Regions, with typically less than 1 second latency for cross-region replication
- Backtrack — Rewind your database to a previous point in time without restoring from a backup (MySQL-compatible only)
When to Choose Aurora over Standard RDS
Choose Aurora when you need higher throughput, more read replicas, global distribution, or want a serverless database option. Aurora is part of the RDS family but is listed separately in the exam because of its unique capabilities.Amazon DynamoDB
Amazon DynamoDB
Amazon DynamoDB is a fully managed, serverless NoSQL database service that delivers consistent single-digit millisecond read and write performance at any scale — from a few requests per second to millions.
Data Model
- Tables — Collections of items (equivalent to rows in SQL)
- Items — Individual records (equivalent to rows)
- Attributes — Data fields within items; each item can have different attributes (schema-less)
- Primary Key — Either a Partition Key alone or a Partition Key + Sort Key combination
Key Capabilities
| Feature | Description |
|---|---|
| DynamoDB Accelerator (DAX) | Fully managed in-memory cache for DynamoDB; reduces read latency from milliseconds to microseconds |
| Global Tables | Multi-region, multi-active replication for global applications with low-latency local reads and writes |
| DynamoDB Streams | Ordered stream of item-level changes for real-time processing with Lambda |
| On-Demand Capacity | Automatically scales to accommodate any traffic level; pay per request |
| Provisioned Capacity | Specify read/write capacity units in advance for cost predictability |
| TTL (Time to Live) | Automatically delete expired items to manage data volume and reduce costs |
When to Use DynamoDB
DynamoDB excels at workloads requiring high-throughput key-value or document lookups at massive scale: gaming leaderboards, IoT telemetry, session stores, shopping carts, and user profile stores.Amazon ElastiCache
Amazon ElastiCache
Amazon ElastiCache is a fully managed in-memory data store and caching service. By caching frequently accessed data in memory, ElastiCache can reduce the latency and load on your primary databases from milliseconds to microseconds.
Supported Engines
| Engine | Best For |
|---|---|
| Redis | Complex data structures, persistence, Pub/Sub, geospatial indexing, sorted sets, multi-AZ replication |
| Memcached | Simple, high-performance caching; horizontal scaling with multi-threaded architecture |
Common Use Cases
- Database query caching — Cache results of expensive SQL queries
- Session management — Store user session data for distributed web applications
- Real-time leaderboards — Redis sorted sets for gaming or analytics rankings
- Rate limiting — Track API call counts per user using Redis atomic counters
For the exam: if a scenario involves caching database results to reduce latency, ElastiCache is the answer. If the question is about reducing DynamoDB latency specifically, DAX (DynamoDB Accelerator) is the more targeted answer.
Amazon Redshift
Amazon Redshift
Amazon Redshift is a fully managed, petabyte-scale cloud data warehouse designed for analytical queries against large datasets. It uses columnar storage and massively parallel processing (MPP) to deliver fast query performance across structured and semi-structured data.
Key Features
- Columnar storage — Stores each column separately, enabling efficient compression and fast aggregation queries
- MPP architecture — Distributes query execution across multiple nodes in parallel
- Redshift Spectrum — Query data directly in S3 without loading it into Redshift
- Redshift Serverless — Run analytics without managing cluster infrastructure
- Concurrency Scaling — Automatically adds capacity to handle query surges
BI Tool Integration
Redshift integrates with Amazon QuickSight, Tableau, Power BI, and other standard SQL-based business intelligence tools via JDBC/ODBC connections.Redshift vs RDS
- RDS → Optimized for transactional workloads (OLTP) — many small read/write operations
- Redshift → Optimized for analytical workloads (OLAP) — complex queries scanning large volumes of historical data
Specialty Databases
Specialty Databases
AWS offers additional managed databases for specific data models and use cases:
Amazon DocumentDB
A fully managed document database service that is MongoDB-compatible. Ideal for storing, querying, and indexing JSON-like documents. Use it when your application uses MongoDB APIs but you want a fully managed service with automatic scaling and Multi-AZ replication.Amazon Neptune
A fully managed graph database service for applications that work with highly connected datasets — such as social networks, fraud detection, recommendation engines, and knowledge graphs. Neptune supports both the Property Graph (Gremlin) and RDF (SPARQL) graph models.Amazon QLDB (Quantum Ledger Database)
A fully managed ledger database that provides a transparent, immutable, and cryptographically verifiable transaction log. Every change to your data is recorded and cannot be deleted or modified. Use QLDB for applications requiring an authoritative audit history: supply chain tracking, financial transactions, HR records.Amazon Timestream
A fully managed time-series database optimized for IoT and operational applications that collect measurements over time. Automatically scales to handle trillions of events per day and includes built-in time-series analytics functions.Relational vs NoSQL Comparison
| Dimension | Relational (RDS / Aurora) | NoSQL (DynamoDB) |
|---|---|---|
| Schema | Fixed schema, predefined columns | Flexible, schema-less items |
| Query Language | SQL — complex joins, aggregations | Key-based lookups; PartiQL (SQL-compatible) |
| Scaling Model | Vertical (scale up instance) | Horizontal (scale out automatically) |
| Consistency | ACID transactions | Eventually consistent by default; strongly consistent available |
| Latency | Milliseconds | Single-digit milliseconds |
| Best For | Complex relationships, reporting, ERP, CRM | High-throughput key-value, IoT, gaming, session data |
Database Selection Quick Reference
| Use Case | Recommended Service |
|---|---|
| Traditional relational app (MySQL / PostgreSQL) | Amazon RDS |
| High-performance relational with global reach | Amazon Aurora |
| Key-value / document at massive scale | Amazon DynamoDB |
| Reduce DynamoDB read latency to microseconds | DynamoDB DAX |
| Cache database query results in memory | Amazon ElastiCache |
| Analytics and business intelligence queries | Amazon Redshift |
| MongoDB-compatible document storage | Amazon DocumentDB |
| Highly connected graph data | Amazon Neptune |
| Immutable audit log / ledger | Amazon QLDB |