Big Data Technologies for Advanced Computing Environments

Geoffrey Holmes

Big Data Technologies for Advanced Computing Environments

Author(s)	Geoffrey Holmes
Country	New Zealand
Abstract	The rapid growth of digital information has created significant demands for computing environments capable of storing, processing, analyzing, and managing massive volumes of heterogeneous data. Traditional computing architectures are increasingly challenged by the scale, velocity, and complexity of contemporary datasets generated through cloud platforms, Internet of Things devices, scientific applications, enterprise systems, intelligent services, and digital infrastructures. Big data technologies provide advanced mechanisms for addressing these requirements through distributed computing, parallel processing, cloud infrastructure, scalable storage, in-memory computing, stream processing, and intelligent analytics. This study examines major big data technologies used in advanced computing environments and evaluates their contribution to scalable data processing, computational efficiency, real-time analytics, intelligent decision-making, and large-scale knowledge discovery. A qualitative descriptive research design based on secondary academic literature was adopted. The study examines distributed file systems, parallel processing frameworks, cloud computing, NoSQL databases, stream-processing technologies, in-memory architectures, and machine-learning platforms. The findings indicate that distributed computing technologies provide the scalability required for processing datasets that exceed the capacity of conventional centralized systems. Cloud-based infrastructures further improve computational flexibility by allowing organizations to dynamically allocate storage and processing resources. Stream-processing technologies support real-time analysis, while NoSQL systems provide flexible mechanisms for managing heterogeneous and rapidly changing information. However, advanced computing environments face challenges involving interoperability, data security, computational complexity, resource management, system reliability, privacy, and technical expertise. The study concludes that effective big data computing requires an integrated technological architecture combining scalable storage, distributed processing, cloud infrastructure, real-time analytics, intelligent algorithms, and strong data governance. Future developments should focus on edge-cloud integration, energy-efficient computing, automated resource allocation, privacy-preserving analytics, and intelligent distributed architectures.
Keywords	Big Data, Advanced Computing, Distributed Computing, Cloud Computing, Parallel Processing, NoSQL, Stream Processing, Data Analytics, Scalable Computing, Intelligent Systems.
Field	Engineering
Published In	Volume 2, Issue 3, May-June 2020
Published On	2020-06-14

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American International Journal of Advanced Research and Development

A Widely Indexed Open Access Peer Reviewed Multidisciplinary Bi-monthly Scholarly International Journal

Big Data Technologies for Advanced Computing Environments

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