International Journal of Science, Engineering and TechnologyAuthors: Dr.C.K. Gomathy, Dasari Venu Gopal, Konkala Krishna Pranav
Abstract: 5G networks generate unprecedented volumes of data due to massive device connectivity, ultra-high-speed communication, and the integration of emerging technologies such as the Internet of Things (IoT), edge computing, and network slicing. Managing this continuous, large-scale, and heterogeneous data stream introduces several challenges related to real-time processing, storage scalability, security, and energy efficiency. While Big Data analytics plays a crucial role in optimizing 5G performance, the stringent latency requirements and distributed nature of 5G infrastructure make conventional data-processing techniques insufficient. This paper presents a comprehensive study of the major Big Data challenges encountered in 5G networks, including data volume explosion, latency constraints, storage limitations, privacy risks, and the complexity of deploying AI/ML models at the network edge. Existing mitigation mechanisms—such as edge computing, software-defined networking (SDN), network function virtualization (NFV), and federated learning—are reviewed in detail. Finally, the paper highlights future research opportunities for achieving intelligent, scalable, and secure Big Data management in next-generation 5G and 6G communication ecosystems.
