An ecommerce company stores customer purchase data in Amazon RDS. The company wants a solution to store and analyze historical data. The most recent 6 months of data will be queried frequently for ana

Creating an Amazon RDS read replica is suitable for offloading read traffic from an OLTP database but is not optimized or cost-effective for large-scale analytical workloads over several terabytes of historical data. It also does not provide a tiered storage solution for the 5-year historical data.

While loading recent data into Amazon Redshift is good for frequent analytics, this option only addresses the most recent 6 months and does not specify a cost-efficient strategy for storing or accessing the entire 5 years of historical data. Keeping 5 years of infrequently accessed data solely in Redshift might be expensive.

This option is identical to option D and provides the same correct solution for establishing a cost-effective data lake foundation in Amazon S3 for historical data.

Incrementally copying data from Amazon RDS to Amazon S3 establishes a cost-effective and scalable data lake foundation for all historical data. Amazon S3 offers extremely low-cost storage for the several terabytes of data, fulfilling the cost optimization requirement for both the frequently accessed recent data and the less frequently accessed older historical data. Analytical services like Amazon Redshift Spectrum can then query this data directly from S3, or a subset can be loaded into an Amazon Redshift cluster for higher performance on frequently accessed data, meeting both performance and cost criteria.