Analyzing Latency Reduction in Cloud-Based Real-Time Data Processing: A Case Study of Edge Analytics Integration

Abstract

As the demand for real-time data processing escalates, cloud computing faces substantial limitations in latency management. This study investigates the integration of edge analytics to enhance data processing speeds in cloud environments. Employing a mixed-method empirical approach, we conducted experiments comparing traditional cloud processing with edge-integrated techniques across multiple data sets. Our findings reveal a significant reduction in response times, with an average latency decrease of 35% while maintaining data integrity. Additionally, qualitative insights from industry experts underline the operational advantages of this paradigm shift, which could reshape data handling in sectors relying on instantaneous decision-making. This research not only addresses critical gaps in the literature surrounding edge computing but also provides actionable insights for practitioners aiming to optimize data workflows in contemporary cloud services.