What You Will Learn

Learn the core concepts and advanced practices for designing, migrating, and operating production database architectures on AWS. Learn how to map workload requirements to the appropriate AWS database service, including relational, document, key-value, wide-column, in-memory, graph, and time-series databases. Practice configuring high availability, scaling read and write capacity, planning and implementing disaster recovery, tuning performance, and securing data at rest and in transit across AWS database services.

Many cloud database failures come from architecture and operational choices, not the database engine itself. They often happen when the workload and database architecture do not fit: a team chooses a relational database for a workload that needs low-latency key-value access, or migrates a self-managed Cassandra cluster without redesigning its capacity, scaling, and operational model. This course helps you identify and avoid those mismatches. From the first lesson, you work through architectural decisions and learn to select, deploy, and operate resilient database systems on AWS.

Introduction to this course

This course is a structured, deliberate progression from foundational data modeling concepts to advanced, multi-region production architectures. It is designed for practitioners who are ready to move beyond basic database provisioning and into enterprise-grade data architecture. Modern cloud databases are distributed systems that require careful orchestration across storage, compute, and networking layers. The decisions we make at the schema and configuration levels determine whether a database scales gracefully or collapses under load.

Throughout this course, we will explore nine primary database services that form the backbone of modern AWS architectures:

• Amazon RDS: Managed relational databases for legacy and commercial engines.

• Amazon Aurora: Cloud-native relational databases with distributed storage.

• Amazon DocumentDB: MongoDB-compatible document databases.

• Amazon DynamoDB: Serverless, high-scale NoSQL key-value and document databases.

• Amazon ElastiCache: Managed in-memory caching for Valkey, Redis OSS, and Memcached.

• Amazon Keyspaces: Serverless, Cassandra-compatible wide-column databases.

• Amazon MemoryDB: Durable, in-memory primary databases.

• Amazon Neptune: Transactional and analytical graph databases.

• Amazon Timestream: Serverless and managed time-series databases.

Each AWS database service is introduced through the workload patterns it is designed to support, and each major architectural decision is evaluated against practical constraints, including latency, cost, data consistency requirements, and operational complexity.

We will move beyond isolated database administration and into designing end-to-end data layers where automation, high availability, and performance tuning are primary concerns. Each lesson builds naturally on the previous one, so the architectural reasoning introduced early (such as decoupling reads from writes using replicas) becomes a foundational principle you will apply again and again across different database engines.

With that framing in place, we can define who this course is designed to serve.

Who this course is built for

This course is for database administrators transitioning to the cloud, backend software engineers who want more data-layer depth, and cloud architects responsible for designing enterprise systems. Prerequisite knowledge includes a basic understanding of database concepts (such as the difference between relational and NoSQL models), foundational SQL knowledge, and introductory AWS knowledge, specifically comfort with VPC networking (subnets and security groups) and IAM for access control.

No prior experience with specific AWS managed databases is assumed, but the pace accelerates quickly into advanced territory. This course focuses on architectural decisions, operational excellence, and distributed-systems thinking rather than just SQL syntax. You will learn how to design a database that scales automatically, survives Availability Zone failures, and integrates cleanly with serverless compute architectures.

Expected learning outcomes

By completing this course, you’ll develop four core competencies that define a cloud data architect:

• Purpose-built database selection: Choose the right database engine based on data model, access patterns, read/write ratios, and latency requirements rather than defaulting to a familiar relational model.

• High availability and disaster recovery: Design resilient architectures using Multi-AZ deployments, read replicas, and Global Databases to survive infrastructure failures and meet strict RTO/RPO targets.

• Performance tuning and scaling: Apply advanced data modeling techniques (like single-table design in DynamoDB), optimize indexes, manage connection storms with proxies, and configure auto scaling for unpredictable workloads.

• Operational excellence: Build systems with least-privilege IAM authentication, implement robust backup and point-in-time recovery (PITR) strategies, and use CloudWatch for continuous observability and bottleneck diagnosis.

These outcomes go beyond basic provisioning and into system-level design thinking, specifically the ability to evaluate trade-offs, anticipate failure modes, and design for resilience at scale.

The course treats databases as stateful components at the center of production systems. They require ongoing architectural planning, observability, and governance. With that context, let’s start with the AWS database services.