What storage solution will best meet the analytics requirements for a company storing customer purchase data for both recent and historical query access?

The recommended answer involves incrementally copying data from Amazon RDS to both Amazon S3 and Amazon Redshift, which provides a comprehensive solution for handling both recent and historical query access efficiently.

This approach leverages the strengths of each service. Amazon RDS (Relational Database Service) is well-suited for transactional applications and provides access to recent data efficiently. However, as data grows, querying historical data directly from RDS may not be optimal due to performance considerations and cost.

By copying data incrementally to Amazon S3, you create a cost-effective storage solution that can retain vast amounts of historical data, enabling analytics workloads without burdensome database access costs. S3 is ideal for storing large datasets, especially those that are infrequently accessed but still necessary for comprehensive analysis.

Loading the recent data into Amazon Redshift facilitates faster analytical queries on current data due to its optimization for complex query handling and large datasets. This combination of RDS for recent transactions, S3 for historical data storage, and Redshift for high-performance analytics strikes a balance between accessibility, cost, and performance.

Overall, this option supports a robust data architecture that accommodates both recent and historical query requirements effectively, making it the best solution.