Big Data Analytics in Computer Science: Technologies, Applications, Challenges and Future Perspectives

Abstract

The rapid expansion of digital technologies has generated unprecedented quantities of structured, semi-structured and unstructured data. This phenomenon has made Big Data Analytics one of the most significant research areas within contemporary computer science. Big Data refers not merely to large datasets but to complex data environments characterized by high volume, velocity, variety and other dimensions such as veracity and value. Traditional data-processing approaches often face limitations when dealing with such datasets, creating a need for distributed storage, parallel processing, advanced algorithms and scalable computational architectures. Big Data Analytics combines computer science, statistics, machine learning, database management and distributed computing to transform large datasets into meaningful information and knowledge. This research paper examines the conceptual foundations of Big Data, major characteristics, technological infrastructure, distributed processing frameworks, data analytics techniques and applications across business, healthcare, education, finance, government and scientific research. Particular attention is given to Hadoop, MapReduce, distributed databases, data mining and machine learning. The paper also examines major challenges involving data quality, privacy, security, scalability, interoperability and computational complexity. The integration of Big Data with cloud computing and artificial intelligence has further expanded its significance and created new possibilities for real-time analytics and intelligent decision-making. The paper concludes that Big Data Analytics has transformed computer science by shifting computational emphasis from isolated datasets toward continuously generated, distributed and heterogeneous information environments. Future developments will depend upon efficient algorithms, secure data management, real-time processing and responsible use of data.