A company stores daily time-series clickstream data in Amazon S3 (millions of rows per day). ML engineers run daily reports and analyze three-day trends with Amazon Athena. Data must be retained for 30 days before archival. Which design gives the highest data retrieval performance?

Partitioning data by date prefix in S3 (e.g., s3://your-bucket/year=YYYY/month=MM/day=DD/) is the most effective strategy for high data retrieval performance with Athena. Athena queries benefit significantly from partition pruning, where it only scans data within the specified date ranges, drastically reducing the amount of data processed and improving query speed and cost. Applying S3 Lifecycle policies to these date-based partitions automates the archival of older data to S3 Glacier Flexible Retrieval, ensuring cost-effective long-term storage without manual intervention. Keeping all data in one bucket without partitions forces Athena to scan the entire dataset for every query, leading to slow performance and high costs. Using Lambda to copy files or storing each day's data in separate buckets are less efficient. Lambda adds unnecessary complexity and cost, while separate buckets complicate data management and querying across multiple days.