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3.3 AWS Database Services

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AWS Certified Cloud Practitioner CLF-C02

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1 Introduction

About the AWS Cloud Practitioner (CLF-C02) Exam Study Strategies and Exam Tips

2 Domain 1: Cloud Concepts (24%)

1.1 What Is Cloud Computing? 1.2 Cloud Deployment Models 1.3 AWS Well-Architected Framework 1.4 Cloud Migration Strategies 1.5 Cloud Economics

3 Domain 2: Security and Compliance (30%)

2.1 The AWS Shared Responsibility Model 2.2 AWS Identity and Access Management (IAM) 2.3 AWS Security Services 2.4 Compliance and Governance 2.5 Network Security

4 Domain 3: Cloud Technology and Services (34%)

3.1 AWS Compute Services 3.2 AWS Storage Services 3.3 AWS Database Services 3.4 AWS Networking and Content Delivery 3.5 Management and Governance Services 3.6 AI/ML and Analytics Services 3.7 Application Integration and Other Services

5 Domain 4: Billing, Pricing, and Support (12%)

4.1 AWS Pricing Models 4.2 Billing and Cost Management Tools 4.3 AWS Support Plans 4.4 Additional Billing Concepts and Services

6 AWS Services Deep Dive

5.1 Compute Services Deep Dive 5.2 Storage and Database Deep Dive 5.3 Security Services Deep Dive 5.4 Architecture Best Practices and Design Principles 5.5 Practice Exam — Mixed Domain Questions

3.3 AWS Database Services

Key Takeaways Explain This Topic AI

Amazon RDS is a managed relational database service supporting MySQL, PostgreSQL, MariaDB, Oracle, SQL Server, and Amazon Aurora.

Amazon DynamoDB is a fully managed NoSQL key-value database with single-digit millisecond latency at any scale.

Amazon Aurora is a cloud-native relational database compatible with MySQL and PostgreSQL, up to 5x faster than standard MySQL.

Amazon Redshift is a cloud data warehouse designed for analytical queries (OLAP) across petabytes of structured data.

Amazon ElastiCache provides in-memory caching (Redis or Memcached) for sub-millisecond response times.

Last updated: October 2026

Quick Answer: AWS offers purpose-built databases rather than one database for everything: RDS/Aurora for relational data, DynamoDB for NoSQL key-value, Redshift for analytics/warehousing, ElastiCache/MemoryDB for in-memory speed, and specialized engines (DocumentDB, Neptune, Keyspaces, Timestream) for documents, graphs, wide-column, and time-series. Pick by data model and access pattern.

  • Relational Databases
  • Amazon RDS (Relational Database Service)

Amazon RDS is a managed service that sets up, operates, patches, and scales relational databases for you, so you avoid the undifferentiated heavy lifting of running a database engine yourself.

Supported engines:

  • Engine Description
  • Amazon Aurora Cloud-native, MySQL/PostgreSQL-compatible
  • MySQL Popular open-source relational database
  • PostgreSQL Advanced open-source relational database
  • MariaDB Community fork of MySQL
  • Oracle Enterprise relational database (BYOL or license-included)
  • SQL Server Microsoft relational database

What RDS manages for you: hardware provisioning, OS patching, database-engine patching, automated backups and point-in-time recovery, Multi-AZ high availability, read replicas, and CloudWatch monitoring.

What you manage: schema design, query tuning, IAM and database-user accounts, instance/storage sizing, and encryption settings. This split is a textbook Shared Responsibility Model example — AWS owns the infrastructure and engine; you own the data and configuration.

Amazon Aurora

Amazon Aurora is AWS's cloud-native relational engine, compatible with MySQL and PostgreSQL but rebuilt for the cloud.

  • Feature Detail
  • Performance Up to 5x the throughput of standard MySQL, up to 3x standard PostgreSQL
  • Durability 6 copies of your data across 3 Availability Zones
  • Auto-scaling storage Grows automatically from 10 GB up to 256 TiB
  • High Availability Automated failover with up to 15 low-latency read replicas
  • Aurora Serverless Automatically scales compute capacity up and down with demand
  • Aurora Global Database Cross-Region replication for low-latency global reads and disaster recovery

On the Exam: Phrases like "cloud-native relational database," "MySQL/PostgreSQL compatible with better performance," or "6 copies across 3 AZs" point to Aurora.

  • NoSQL Databases
  • Amazon DynamoDB

Amazon DynamoDB is a fully managed, serverless NoSQL database delivering single-digit millisecond latency at any scale.

  • Feature Detail
  • Data Model Key-value and document
  • Performance Single-digit millisecond response, microseconds with DAX caching
  • Scaling Automatic; handles tens of trillions of requests per day
  • Serverless No servers to provision, patch, or maintain
  • Global Tables Multi-Region, multi-active replication
  • Pricing On-demand (pay per request) or provisioned capacity

When to use DynamoDB: high-traffic web/mobile/gaming backends, IoT data, session stores, and shopping carts — anywhere you need predictable low latency at massive scale without managing servers.

On the Exam: DynamoDB = NoSQL, serverless, single-digit millisecond latency, key-value/document. A scenario describing a non-relational, internet-scale, low-latency store is almost always DynamoDB.

  • Analytical Databases
  • Amazon Redshift

Amazon Redshift is a cloud data warehouse for online analytical processing (OLAP) across very large datasets.

  • Feature Detail
  • Type Columnar, massively parallel data warehouse
  • Scaling Petabyte-scale
  • Redshift Serverless Run analytics without managing clusters
  • Redshift Spectrum Query data directly in S3 without loading it first

When to use Redshift: business intelligence, reporting, and complex aggregations over historical data. Contrast with RDS/Aurora, which are tuned for transactional (OLTP) workloads.

  • Caching and In-Memory
  • Amazon ElastiCache

Amazon ElastiCache provides managed in-memory caching to cut latency and database load.

  • Engine Use Case
  • Redis Advanced data structures, replication, pub/sub, persistence
  • Memcached Simple, multi-threaded caching with no persistence

Use ElastiCache for repetitive query results, shared session state, leaderboards, and any need for sub-millisecond reads. Amazon MemoryDB for Redis is a related, durable Redis-compatible database when you need in-memory speed plus durability.

Other Database Services

  • Service Type Use Case
  • Amazon DocumentDB Document (MongoDB-compatible) Content management, catalogs, user profiles
  • Amazon Neptune Graph Social networks, fraud detection, recommendations
  • Amazon Keyspaces Wide-column (Cassandra-compatible) Fleet, maintenance, route data
  • Amazon Timestream Time-series IoT, DevOps monitoring, application metrics

Amazon QLDB (retired) Ledger (immutable) Support ended July 31, 2025; AWS points ledger and audit workloads to Aurora PostgreSQL

  • Amazon MemoryDB for Redis Durable in-memory Redis workloads needing durability
  • Database Selection Guide
  • Requirement Best Service
  • Relational data, complex joins/transactions Amazon RDS or Aurora
  • High-speed key-value lookups at scale Amazon DynamoDB
  • Data warehousing and analytics (OLAP) Amazon Redshift
  • In-memory caching, sub-ms reads Amazon ElastiCache
  • Document storage (MongoDB workloads) Amazon DocumentDB
  • Highly connected/graph relationships Amazon Neptune
  • Immutable, cryptographically verifiable log Amazon QLDB in older material (retired in 2025)
  • Time-series data (sensors, metrics) Amazon Timestream

On the Exam: Read the data model in the scenario. "Relational/SQL" -> RDS/Aurora. "Key-value/NoSQL" -> DynamoDB. "Analytics/warehouse" -> Redshift. "Cache/fastest reads" -> ElastiCache. "Graph" -> Neptune. "Immutable ledger" -> QLDB in older questions only: QLDB was retired in 2025 and is not on the online in-scope list.

Managed Service Value and Migration

A recurring exam theme is why teams choose a managed database over running their own on EC2. With RDS, Aurora, or DynamoDB, AWS removes operational toil: it patches the OS and engine, takes automated backups, supports point-in-time recovery, and offers one-click high availability. The trade-off is less low-level control, but for most workloads that control is undifferentiated heavy lifting better left to AWS.

To move an existing database into these services, AWS provides the Database Migration Service (DMS) for near-zero-downtime data migration and the Schema Conversion Tool (SCT) when the source and target engines differ (for example, Oracle to Aurora PostgreSQL). Together they let organizations leave self-managed databases behind while keeping applications online. Expect a scenario that pairs "migrate with minimal downtime" (DMS) with "convert the schema between engines" (SCT).