Big Data Analytics: Technologies, Applications, and Future Prospects

• Author(s): Chioma Susan Nwaimo ; Oluchukwu Modesta Oluoha ; Oyewale Oyedokun
• Paper ID: 1709354
• Page: 411-438
• Published Date: 31-05-2019
• Published In: Iconic Research And Engineering Journals
• Publisher: IRE Journals
• e-ISSN: 2456-8880
• Volume/Issue: Volume 2 Issue 11 May-2019

Abstract

Big Data Analytics (BDA) has emerged as a transformative force across industries, reshaping how organizations generate value from the voluminous, fast-moving, and diverse data produced daily. The evolution of data-centric technologies since the early 2010s has catalyzed a paradigm shift in how insights are derived, with 2019 marking a critical point of convergence between maturing analytics frameworks and accelerated adoption across sectors. This journal explores the core technologies underpinning BDA, such as distributed computing, cloud infrastructures, and advanced machine learning algorithms, detailing how they collectively facilitate the extraction of meaningful patterns and predictions from large datasets. It evaluates the interplay between structured and unstructured data environments, storage mechanisms such as Hadoop Distributed File System (HDFS), and processing engines like Apache Spark, in driving scalable and real-time analytics capabilities. Furthermore, the paper assesses real-world applications of BDA across domains such as healthcare, finance, smart cities, e-commerce, and cybersecurity. It outlines how data analytics has enhanced precision in clinical decision-making, improved fraud detection in financial services, and enabled intelligent systems in urban governance. By integrating case examples and empirical studies, the research highlights both the accomplishments and limitations of current BDA implementations. The role of data governance, ethical considerations, and regulatory frameworks are also considered, recognizing the importance of responsible data practices in a globally connected world. Looking toward the future, the journal identifies emerging frontiers such as quantum analytics, edge computing, and federated learning, which promise to redefine the limits of big data processing and insight generation. The work aims to provide a comprehensive scholarly foundation for understanding the technological, practical, and theoretical dimensions of big data analytics as of 2019, while laying the groundwork for future research in the evolving data ecosystem.

Keywords

Big Data Analytics, Machine Learning, Distributed Computing, Apache Spark, Cloud Infrastructure, Data Governance, Real-Time Analytics, Emerging Technologies, 2019 Trends.

Citations

IRE Journals:
Chioma Susan Nwaimo , Oluchukwu Modesta Oluoha , Oyewale Oyedokun "Big Data Analytics: Technologies, Applications, and Future Prospects" Iconic Research And Engineering Journals Volume 2 Issue 11 2019 Page 411-438

IEEE:
Chioma Susan Nwaimo , Oluchukwu Modesta Oluoha , Oyewale Oyedokun "Big Data Analytics: Technologies, Applications, and Future Prospects" Iconic Research And Engineering Journals, 2(11)