Managing query timeouts and high storage costs in data-intensive applications, particularly in environments with billions of rows, presents substantial challenges, as demonstrated by the Harness dashboards powered by Looker. These dashboards, which handle over 4 billion rows in a PostgreSQL-backed TimescaleDB, initially experienced significant delays of 10-15 minutes per query due to inefficient querying and large data volumes. To address these issues, the team implemented aggregated tables that pre-compute summaries, thereby significantly reducing the number of rows queried and improving performance. By leveraging parallel Common Table Expressions (CTEs), time-based partitioning, and proper indexing, query times were slashed from 15 minutes to just 15 seconds. The transition involved a one-time data migration and the establishment of daily aggregation jobs, ensuring up-to-date information and enhanced user experience. This transformation highlights the importance of data aggregation, compression, and optimized query strategies in managing large-scale analytics systems efficiently.