The surge of Internet of Things (IoT) devices and the rapid increase in data generation have required improvements in big data processing techniques. Conventional cloud-based systems, although resilient, frequently face issues concerning latency, bandwidth limitations, and real-time processing. Edge computing represents a revolutionary model that positions computation and data storage in proximity to the data source. By leveraging edge computing's proximity to data sources, this study aims to reduce latency, enhance data processing speeds, and improve overall system efficiency. The findings highlight the potential of edge-based architectures in addressing the challenges of traditional cloud-based models, particularly in time-sensitive applications. This paper examines strategies for enhancing latency and performance in edge-based big data architectures. We illustrate how edge computing can transform real-time data processing through theoretical insights and statistical references.