Title: Benchmarking the Scalability of Modern SQL Analytics Engines for Business Intelligence Abstract: This paper presents an empirical performance assessment of contemporary distributed SQL query engines designed for big data processing in business intelligence applications. Through a micro-benchmarking framework, we evaluate Shark, Impala, and Hive using both synthetic and real-world datasets representative of SME workloads. The study measures processing power, resource utilization, and scalability, revealing significant performance variations across engines. Our findings show that while Hive is consistently outperformed, the choice between Shark and Impala depends heavily on query type and data volume, with scalability gains plateauing beyond a moderate cluster size. These insights provide actionable guidance for organizations selecting scalable SQL engines for data-driven decision-making.