CLF-C02
Deep Dive
AWS offers a full portfolio of managed databases for different access patterns. The CLF-C02 especially tests RDS Multi-AZ vs Read Replicas, and RDS vs DynamoDB as a choice scenario.
Contents
RDS / Aurora
Relational SQL
DynamoDB
NoSQL key-value
ElastiCache
In-memory / Cache
Redshift
Data Warehouse
Managed relational databases on AWS
Amazon RDS makes it easy to set up, operate, and scale relational databases in the cloud. AWS manages the server, the operating system, engine patching, and automated backups.
Supported engines: MySQL, PostgreSQL, Oracle, Microsoft SQL Server, MariaDB, Amazon Aurora
What AWS manages in RDS
What the customer manages in RDS
RDS Multi-AZ vs Read Replicas — The most important distinction on the exam
✓ Multi-AZ — High Availability
RDS creates a synchronous replica in another Availability Zone. If the primary instance fails, RDS automatically fails over to the secondary in 1-2 minutes.
Question: "Do I want the DB to keep running if an AZ fails?" → Multi-AZ
✓ Read Replicas — Read Scaling
RDS creates asynchronous read-only replicas. Applications can direct read queries to the Read Replicas, reducing the load on the primary instance.
Question: "My app reads a lot of data and I need to scale?" → Read Replicas
An external pentest found that the production database accepts direct connections from the internet. Find and fix the Security Group — no guided steps.
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AWS's own relational database engine, compatible with MySQL and PostgreSQL
Amazon Aurora is a relational database built by AWS from the ground up for the cloud. It's compatible with MySQL and PostgreSQL but delivers significantly higher performance.
5x
Faster than MySQL
3x
Faster than PostgreSQL
up to 15
Read Replicas per cluster
10GB → 128TB
Storage auto-scaling
Aurora vs standard RDS
Aurora Serverless
Automatically scales database capacity based on demand. Ideal for apps with unpredictable or intermittent load. You pay only for resources used during active usage.
A fully managed, serverless key-value and document database
Amazon DynamoDB is a key-value and document database that delivers single-digit millisecond performance at any scale. It's serverless — there are no servers to manage, provision, or patch.
DynamoDB characteristics
DynamoDB Accelerator (DAX)
A fully managed, in-memory cache compatible with DynamoDB. Improves performance by up to 10x for read-heavy workloads.
Create a DynamoDB table in on-demand mode and understand the key-value data model.
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Managed in-memory caching (Redis and Memcached)
ElastiCache is a fully managed in-memory caching service. It improves database performance by caching frequently queried data.
Redis
Memcached
A data warehouse for petabyte-scale data analytics
Amazon Redshift is a fully managed, scalable, high-performance data warehouse. Designed for analyzing large volumes of data (OLAP), not for real-time transactions (OLTP).
Use cases (OLAP)
Not meant for (OLTP)
Amazon DocumentDB
Document DB (MongoDB compatible)
A fully managed JSON document database, compatible with MongoDB workloads. Scales storage automatically.
When: Migrating from MongoDB, content apps, product catalogs
Amazon Neptune
Graph Database
A managed graph database. Optimized for storing and querying highly connected relationships between data.
When: Social networks, recommendation engines, fraud detection, knowledge graphs
Amazon QLDB
Ledger Database (blockchain-like)
An immutable, cryptographically verifiable ledger database. It reached end of support on July 31, 2025; AWS recommends migrating to Aurora PostgreSQL.
When: Financial systems, transaction logging, supply chain tracking
| Criterion | RDS / Aurora (SQL) | DynamoDB (NoSQL) |
|---|---|---|
| Data model | Tables with relationships (JOIN) | Key-value or JSON documents |
| Schema | Rigid (schema-on-write) | Flexible (schema-on-read) |
| Scaling | Vertical (more CPU/RAM) or Read Replicas | Automatic, unlimited horizontal scaling |
| ACID transactions | Yes, native | Yes (DynamoDB transactions, limited) |
| Complex queries | Full SQL: JOINs, GROUP BY, subqueries | Only by partition key / sort key. No native JOINs. |
| Performance under load | Degrades with many JOINs and high concurrency | Consistent performance at any scale |
| Use cases | ERP, CRM, finance, transactional e-commerce | Gaming, IoT, user profiles, sessions, real time |
AWS Database Migration Service (DMS)
Migrates data with minimal downtime
Migrates databases to AWS (or between AWS services) while the source system stays operational during the migration. Supports both continuous and one-time migrations.
Homogeneous migration
Same engine: on-prem MySQL → RDS MySQL. Just DMS, no schema conversion.
Heterogeneous migration
Different engines: Oracle → Aurora PostgreSQL. Requires SCT for the schema + DMS for the data.
AWS Schema Conversion Tool (SCT)
Automatically converts the database schema (tables, views, indexes, stored procedures) from one engine to another.
SCT + DMS together
SCT converts the SCHEMA (structure). DMS migrates the DATA (content). Used together for heterogeneous migrations.
Common use cases
On-premises Oracle → Amazon Aurora PostgreSQL
Tools: SCT (schema) + DMS (data)
On-premises SQL Server → Amazon RDS for SQL Server
Tools: DMS only
On-premises MySQL → Amazon RDS for MySQL
Tools: DMS only
Teradata data warehouse → Amazon Redshift
Tools: SCT (schema) + DMS (data)
Deploy a managed relational database with Amazon RDS.
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Test what you just learned
A company has an RDS MySQL database with performance problems during peak hours. Most of the traffic is read queries. Which Amazon RDS solution improves read performance without affecting the availability of the production database?
Put what you learned to the test with original CLF-C02 exam questions.
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