Overview of AWS DMS and its purpose

Key features: Minimal downtime, heterogeneous and homogeneous migrations

Use cases: Database migration, consolidation, and continuous replication

Types of database migrations: Homogeneous vs. Heterogeneous

Supported source and target databases (e.g., MySQL, PostgreSQL, Oracle, SQL Server, Amazon Aurora, etc.)

Migration strategies: Lift and shift, re-platforming, refactoring

Creating a DMS replication instance

Configuring source and target endpoints

Setting up VPC, security groups, and IAM roles for DMS

Understanding and configuring DMS tasks: Full load, CDC (Change Data Capture), and full load + CDC

Introduction to AWS SCT and its role in heterogeneous migrations

Converting database schema and code (stored procedures, triggers, etc.)

Handling schema conversion challenges and best practices

Applying schema changes to the target database

Setting up ongoing replication using DMS

Configuring change data capture (CDC) for real-time replication

Managing and monitoring replication tasks

Use cases for continuous data replication: Disaster recovery, data consolidation, etc.

Understanding DMS performance metrics and tuning parameters

Best practices for optimizing migration speed and resource usage

Managing large-scale migrations and handling bulk data loads

Using pre-migration assessments to identify potential bottlenecks

Securing DMS resources with IAM roles and policies

Encrypting data in transit using SSL/TLS

Managing access control for source and target databases

Ensuring compliance with industry standards during migration

Integrating DMS with AWS S3, Redshift, and RDS

Using AWS Glue for ETL (Extract, Transform, Load) during migration

Automating migration workflows with AWS Lambda and Step Functions

Combining DMS with CloudWatch for monitoring and alerting