Which solution will meet these requirements?
Set up an Amazon DynamoDB database and a DynamoDB Accelerator (DAX) cluster.
Set up an Amazon RDS database and an Amazon ElastiCache for Redis cluster. Implement a lazy loading caching strategy with ElastiCache.
Setup an Amazon DynamoDB database that has an in-memory cache. Implement a lazy loading caching strategy in the application.
Set up an Amazon RDS database and an Amazon DynamoDB Accelerator (DAX) cluster. Specify a TTL setting for the DAX cluster.
Explanations:
Amazon DynamoDB is a NoSQL database designed for high availability and performance. By using DynamoDB Accelerator (DAX), which is an in-memory caching layer for DynamoDB, the application can achieve microsecond retrieval times. DAX automatically handles cache invalidation when items are added, changed, or deleted in DynamoDB, ensuring data consistency.
Amazon RDS is a relational database service, which may not achieve microsecond retrieval times as efficiently as DynamoDB for frequently accessed products. While ElastiCache for Redis can provide fast caching, a lazy loading strategy may introduce latency during cache misses, and maintaining cache consistency on updates can be complex.
Although Amazon DynamoDB supports in-memory caching strategies, implementing lazy loading in the application might not guarantee microsecond retrieval times. Additionally, lazy loading can introduce delays during cache misses, and manual cache invalidation would be necessary to ensure consistency when products change.
Amazon RDS with DAX is an incorrect pairing since DAX is specifically designed for DynamoDB. RDS does not utilize DAX, and while RDS may offer some performance benefits, it is less optimized for microsecond retrieval compared to DynamoDB with DAX. Moreover, the TTL setting in DAX is not applicable for RDS.
I plot that the answer is:
Set up an Amazon DynamoDB database and a DynamoDB Accelerator (DAX) cluster.