A Comprehensive Review Of Real-Time Analytics Techniques And Applications In Streaming Big Data

Abstract

The rapid expansion of data generation across industries has made real-time analytics essential for timely decision-making and operational efficiency. This review paper examines the current landscape of real-time analytics techniques for processing streaming big data, focusing on approaches that enable high-speed data ingestion, storage, and processing in a continuously evolving data environment. We reviewed a total of 50 articles, encompassing a range of methodologies, applications, and system architectures that support real-time analytics. Key findings highlight advancements in stream processing frameworks, machine learning models for real-time predictions, and challenges associated with data scalability and latency. Applications are particularly prominent in sectors such as finance, healthcare, and urban planning, demonstrating the transformative impact of real-time insights on industry performance. This review contributes to a deeper understanding of real-time data handling techniques, addressing critical areas for future research and development.