Title: Innovations and Discoveries in the Multidisciplinary Research
Chief Editor: Dr. Padmavathi S. M.
Associate Editor: Dr. Poonam Sachin Kadlag
Co-Editor: Dr. Shailaja A Akkur
ISBN: 978-81-69857-36-9
Chapter: 3
DOI: https://doi.org/10.59646/785/3
Authors: Dr. J. Antony John Prabhu, Dr. K. Loura Jency, Dr. S. Lakshmanan, Dr. V. A. Jane, and Dr. S. Sathyapriya
Abstract
The rapid growth of digital technologies has led to an unprecedented increase in the volume, variety, and velocity of data generated across industries. Organizations now rely on big data architectures and advanced analytics frameworks to transform large and complex datasets into meaningful insights that support strategic decision-making. This review examines the evolution of big data architectures, highlighting the transition from traditional centralized systems to modern distributed computing environments capable of handling massive datasets with improved reliability and performance. It also explores widely adopted analytics frameworks, including batch processing, stream processing, and hybrid models, emphasizing their role in enabling real-time and large-scale data analysis. The study further discusses the key factors influencing scalability and efficiency in big data systems, such as distributed storage, parallel computing, resource management, fault tolerance, and cloud-based infrastructure. Emerging technologies, including artificial intelligence, machine learning, edge computing, and containerized environments, are also examined for their growing contribution to enhancing the speed, flexibility, and intelligence of big data platforms. While these advancements offer significant opportunities, challenges related to data security, privacy, interoperability, governance, and operational complexity remain important concerns that organizations must address. Overall, the review concludes that selecting an appropriate big data architecture and analytics framework requires careful consideration of organizational objectives, workload characteristics, and resource availability. A well-designed and scalable architecture not only improves processing efficiency and analytical accuracy but also enables organizations to respond more effectively to changing business needs. The findings provide valuable insights for researchers, practitioners, and decision-makers seeking to develop high-performance, scalable, and sustainable big data solutions in an increasingly data-driven world.
Keywords: Big Data, Big Data Architecture, Analytics Frameworks, Scalability, Distributed Computing, Cloud Computing, Batch Processing, Stream Processing, Apache Hadoop, Apache Spark, Machine Learning, Edge Computing, Fault Tolerance, Resource Management.