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Choosing between Amazon Aurora, Amazon Relational Database Service (Amazon RDS), Amazon DynamoDB, Amazon ElastiCache, and Amazon Redshift starts with one decision. We must define how the workload reads and writes data, and what it must guarantee for consistency, latency, availability, and recovery. In this course, we will build a repeatable framework that turns those requirements into a database choice we can justify and verify.
This course stays focused on five AWS managed data services and the workloads they fit. Amazon Aurora and Amazon RDS are for online transaction processing in relational engines. Amazon DynamoDB is for key-value access patterns with predictable single-digit millisecond performance at scale. Amazon ElastiCache is an in-memory cache that stores copies of data rather than the system of record. Amazon Redshift is for analytics workloads that scan and aggregate large datasets.
We also keep a clear AWS boundary in mind. A Region is a geographic area where AWS runs multiple, isolated data centers. An Availability Zone is one isolated location within a Region. Many durability and availability features depend on how a service uses multiple Availability Zones inside one Region.
Let’s inspect how these five services sit on the transaction, cache, and analytics spectrum.
We will use the same decision loop in every lesson. First we state the access patterns and data model. Then we map those needs to a service mechanism that provides them. Next we name the tradeoffs in cost, operational ownership, and failure behavior. Finally we confirm the choice using observable evidence, not assumptions.
Evidence must match the boundary we are testing. Metrics and logs show what happened in a real runtime. Static analysis and configuration review only show what could happen given a configuration. A good decision includes at least one runtime signal that can falsify our assumptions.
Let’s visualize the requirements-to-mechanism-to-evidence path we will practice repeatedly.