Sarcouncil Journal of Engineering and Computer Sciences

Abstract: The integration of SQL and the Google Maps API in modern cloud software systems presents a powerful approach to combining structured and geospatial data for enhanced decision-making and operational efficiency. This study explores techniques for seamless data integration, focusing on performance, scalability, and real-world applicability. Through a mixed-methods approach, the research evaluates key metrics such as data retrieval time, query execution time, system latency, and resource utilization under varying conditions. A logistics management case study demonstrates the practical benefits of this integration, achieving a 15% reduction in delivery time and a 10% improvement in fuel efficiency. Statistical analysis reveals that stream processing offers the lowest latency and highest scalability, making it ideal for real-time applications, while data virtualization and ETL provide flexibility for batch processing scenarios. The findings highlight the importance of optimization techniques, such as caching and batch processing, in improving integration performance. This research provides actionable insights for developers and organizations aiming to modernize their cloud systems through effective data integration strategies