Summary and Quiz
Explore key AWS database models, decision frameworks, and essential operational skills to manage cloud databases effectively. Understand how to select the right service, plan capacity, monitor performance, and apply best practices for scalable, secure AWS data solutions.
This chapter covered the full life cycle of AWS database decision-making, from understanding fundamental database models and their AWS service mappings, through shared infrastructure controls, a structured selection framework, and the day-to-day operational skills required to keep databases running reliably at scale.
Database Model Fundamentals
AWS offers multiple database models, each optimized for specific data structures and access patterns. Relational databases such as Aurora and RDS provide fixed schemas and ACID transactions for OLTP workloads, while document databases like DocumentDB and DynamoDB support flexible JSON-based data. DynamoDB also serves as a key-value database for low-latency access, and Amazon Keyspaces provides a wide-column model for large-scale telemetry and event data. ElastiCache and MemoryDB deliver in-memory performance for caching and real-time applications, Neptune enables graph-based relationship analysis, and Timestream is purpose-built for time-series data. Selecting the right database depends on workload characteristics, including consistency, latency, durability, and scalability requirements. Modern architectures often embrace polyglot persistence, combining multiple database services, such as Aurora for transactions, DynamoDB for user profiles, Neptune for ...