The 473,582-row dataset processed through 15 SQL queries suggests compute concentration in data retrieval and aggregation rather than synthesis, since ranking data typically requires filtering, sorting, and join operations across two related tables. This workload is well-suited for scheduled runs given the deterministic nature of SQL operations on static ranking tables; on-demand execution would be acceptable only if query results are cached to avoid redundant computation. The primary lever for cost reduction is query optimization—specifically, pre-aggregating ranking metrics and eliminating redundant table scans, which would reduce both SQL execution time and the downstream data volume fed to synthesis operations.