Enhancing SQL Query Performance: A Case Study on Optimizing Enterprise Data Processing

The performance of SQL queries is what makes enterprise data systems scalable and efficient. As companies use more and more complex queries on databases that are spread out and in the cloud, problems like bad indexing, wrong cardinality estimates, and slow execution plans become critical. This paper shows a real-world case study of an enterprise application that uses SQL Server and PostgreSQL to manage terabyte-scale hybrid datasets. We look at how targeted optimization techniques like indexing strategies, query refactoring, execution plan analysis, and partitioning affect real-world query workloads. Quantitative results show that query latency has improved by up to 60%, and CPU and I/O usage have gone down by a lot. The study also includes learned models for cardinality estimation and plan selection, which show how useful machine learning-enhanced optimization can be in real-world situations. These results show how important it is to proactively tune systems and give system architects and database administrators useful tips on how to improve performance in large-scale deployments.