Big Data Processing Efficiency in Edge-Cloud Environments: A Hybrid Approach

Abstract

The rapid growth of data in the digital age poses major challenges in the management and processing of big data, especially in terms of time efficiency, resource utilization, and overall system performance. The study aims to explore the extent to which hybrid edge-cloud architectures can improve the efficiency of big data processing compared to traditional cloud-only models. Using a qualitative approach and exploratory case study design, data was collected through semi-structured interviews, direct observation, and technical document analysis. The results show that the hybrid edge-cloud approach is able to significantly increase efficiency through decentralization of computing processes, reduction of the load on the network and cloud, and optimization of bandwidth usage and energy consumption. However, there are also technical challenges that need to be overcome, such as data synchronization between systems and interoperability of multiple platforms, which require a mature and structured implementation strategy. In addition to the technical benefits, hybrid systems also received positive responses from users because of their more responsive and stable performance than traditional solutions. This study concludes that the success of the efficiency of edge-cloud systems depends not only on technological sophistication, but also on organizational readiness, operational strategy, and user acceptance and adaptation levels. Therefore, hybrid edge-cloud architecture is highly relevant to be applied in various sectors that require real-time data processing, such as the manufacturing industry, smart city development, and digital healthcare, to support fast and accurate decision-making and improve service quality.