Curriculum Overview: AWS Relational Database Services (RDS & Aurora)

This curriculum provides a comprehensive pathway to mastering relational database identification and selection within the AWS ecosystem, specifically focusing on the differences between managed and unmanaged hosting and the unique capabilities of Amazon Aurora.

Prerequisites

Before starting this module, students should have a baseline understanding of the following:

• Basic Database Concepts: Understanding of tables, rows, columns, and Structured Query Language (SQL).
• Cloud Computing Fundamentals: Familiarity with the AWS Shared Responsibility Model and basic cloud benefits (scalability, elasticity).
• Networking Basics: A high-level understanding of Virtual Private Clouds (VPC), subnets, and security groups.
• EC2 Knowledge: Understanding how Virtual Machines (EC2) work, as this forms the comparison point for managed services.

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Module Breakdown

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Learning Objectives per Module

Module 1: Foundations

• Identify the structure of a relational database (SQL).
• Distinguish relational databases from NoSQL (DynamoDB).

Module 2: Managed vs. Unmanaged

• Explain the benefits of Amazon RDS (patching, backups, setup) over manual installation on Amazon EC2.
• Understand the administrative trade-offs of the "Managed Service" approach.

Module 3: RDS Engines & Licensing

• Recall the specific RDBMS engines supported by RDS.
• Differentiate between License Included (SQL Server, Oracle) and BYOL models.

Module 4: Amazon Aurora

• Explain why Aurora is considered "Cloud Native."
• Describe Aurora's performance improvements (up to 5x MySQL throughput).

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Visual Anchors

Decision Tree: RDS vs. Self-Managed (EC2)

Aurora Data Redundancy

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Examples Section

Case Study 1: Legacy ERP Migration

• Scenario: A company uses a legacy Microsoft SQL Server database on-premises. They want to move to the cloud without rewriting their application code.
• Solution: Amazon RDS for SQL Server.
• Benefit: They can use the "License Included" model and avoid the overhead of managing Windows Server updates while keeping their existing SQL schema.

Case Study 2: High-Traffic Web Application

• Scenario: A global social media platform requires massive read scaling and cannot afford downtime.
• Solution: Amazon Aurora.
• Benefit: Aurora provides Read Replicas for horizontal scaling and maintains 6 copies of data across 3 AZs, ensuring that even an entire AZ failure won't cause data loss.

[!TIP] Use Amazon Aurora if you need MySQL or PostgreSQL compatibility but require enterprise-grade performance and durability that exceeds standard RDS instances.

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Success Metrics

To demonstrate mastery of this curriculum, a student must be able to:

1. List all 7 RDS Engines: Db2, Aurora, PostgreSQL, MySQL, MariaDB, Oracle, and Microsoft SQL Server.
2. Explain RDS Storage: Identify that RDS uses EBS (Elastic Block Store) volumes for storage.
3. Define Aurora Compatibility: State that Aurora is specifically compatible with MySQL and PostgreSQL.
4. Identify High Availability: Explain how Multi-AZ deployments and Read Replicas differ in function (availability vs. performance).
5. Contrast Relational vs. NoSQL: Explain that RDS is for structured data with schemas, whereas DynamoDB is for schemaless, key-value storage.

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Real-World Application

• E-Commerce: Managing inventory and customer orders where ACID compliance (Atomicity, Consistency, Isolation, Durability) is non-negotiable.
• Financial Services: Using RDS with Multi-AZ for disaster recovery to ensure financial records are never lost.
• Data Migration: Using the AWS Schema Conversion Tool (SCT) to switch from an expensive commercial database (like Oracle) to a cost-effective cloud-native one (like Aurora).

$\text{Availability} = \frac{\text{Uptime}}{\text{Uptime} + \text{Downtime}}$ Amazon Aurora is designed for $> 99.99\%$ availability.