Curriculum Overview: AWS Database Services & Migration

This document outlines the curriculum for mastering AWS database offerings and the mechanisms for migrating on-premises workloads to the cloud. It covers relational, NoSQL, and in-memory solutions, alongside the tools required for seamless data transition.

## Prerequisites

Before engaging with this module, students should possess:

• Cloud Fundamentals: Basic understanding of the AWS Global Infrastructure (Regions and Availability Zones).
• Compute Basics: Knowledge of Amazon EC2 and the Shared Responsibility Model.
• Networking Foundations: Understanding of VPCs, subnets, and security groups.
• General Database Literacy: Familiarity with the concepts of Relational (SQL) vs. Non-Relational (NoSQL) data structures.

## Module Breakdown

## Module Objectives

Module 1: Relational Databases

• Decide when to use EC2-hosted databases (Full Control) vs. AWS Managed Services (RDS/Aurora).
• Identify the six supported engines: MySQL, MariaDB, Oracle, PostgreSQL, Microsoft SQL Server, and Amazon Aurora.
• Understand the proprietary benefits of Amazon Aurora (MySQL/PostgreSQL compatibility, 64TB auto-scaling storage).

Module 2: NoSQL and Performance

• Define NoSQL characteristics using Amazon DynamoDB (Key-value pairs, document data types, horizontal scaling).
• Recognize use cases for memory-based databases like Amazon ElastiCache to reduce latency.

Module 3: Migration Frameworks

• Distinguish between homogeneous (same engine) and heterogeneous (different engine) migrations.
• Utilize the AWS Schema Conversion Tool (SCT) for mapping source schemas to target engines.
• Execute migrations using AWS Database Migration Service (DMS) to ensure minimal downtime.

## Visual Anchors

AWS Database Selection Logic

The Migration Workflow

## Success Metrics

To demonstrate mastery of this curriculum, the learner must be able to:

• Calculate RPO: Determine the Recovery Point Objective; for example, realizing that an RPO of <5 minutes requires Multi-AZ synchronous replication.
• Migration Planning: Correctly select SCT for schema transformation and DMS for data movement.
• Architecture Selection: Choose the correct licensing model (BYOL vs. License Included) for Oracle or SQL Server workloads.
• Scaling Identification: Identify that DynamoDB provides seamless horizontal scaling whereas RDS typically scales vertically or via Read Replicas.

## Real-World Application

[!IMPORTANT] AWS Database services are not just about storage; they are about offloading "undifferentiated heavy lifting."

• Enterprise Modernization: A company moves a legacy SQL Server to Amazon Aurora to reduce licensing costs and improve performance via 6-way data replication.
• Retail Peak Loading: An e-commerce site uses Amazon DynamoDB to handle millions of requests during Black Friday without manual intervention to scale the database.
• Global Continuity: Using Multi-AZ deployments to ensure that if a lightning strike hits one data center, the database automatically fails over to another Availability Zone without losing data.

## Examples Section

Homogeneous vs. Heterogeneous Migrations

• Homogeneous Example: Moving a local MySQL instance to RDS for MySQL. Since the engine is the same, AWS DMS can move the data directly without needing the Schema Conversion Tool.
• Heterogeneous Example: Moving an Oracle database to Amazon Aurora (PostgreSQL). This requires the Schema Conversion Tool (SCT) to rewrite the stored procedures and table schemas before DMS can migrate the data.

Database Engine Decision Matrix