In the evolving landscape of analytics, the demand for high queries per second (QPS) is becoming increasingly significant as companies like Confluent, Target, and Pinterest integrate analytics into their operations, extending beyond traditional reports and dashboards. High QPS in analytics differs from high concurrency, focusing on processing many queries quickly rather than merely handling multiple simultaneous queries. Achieving high QPS is challenging due to the complex nature of queries, the volume of data processed, and the architecture of traditional data warehouses like Snowflake, BigQuery, and Redshift, which often prioritize cost efficiency over speed. Simply increasing hardware resources does not guarantee sub-second query performance, as efficient processing, strategic data storage, and minimized operational burdens are crucial. Apache Druid is highlighted as a solution specifically designed for high-performance, real-time analytics, offering low latency and cost-effective performance, as demonstrated by its use in large-scale applications at companies like Target and Confluent.