New Framework Modeling for Big Data Analysis of the Future

Abstract

The area of big data analysis confronts several obstacles in its quest to derive useful insights from the ever-increasing amount and complexity of available data. To cope with the future volume, velocity, and diversity of data, new frameworks and models must be created. In this article, the authors offer a new framework for big data analysis that makes use of a variety of recently developed tools and techniques specifically designed to meet these demands. The three main pillars of our methodology are data acquisition, data processing, and data analysis. To ensure effective and continuous data collection from many sources, the authors make use of recent developments in data streaming and real-time data processing methods. This guarantees that the framework can process large amounts of data quickly enough to allow for timely analysis. The authors do tests using real-world, large-scale data sets to see how well this suggested framework performs in practice. When compared to conventional methods, the results show dramatic enhancements in terms of processing velocity, scalability, and precision. The authors also emphasize the framework's potential for integration with cutting-edge technologies like edge computing and internet of things (IoT) gadgets, as well as its flexibility to accommodate shifting data landscapes. Enhanced decision-making and insights in the age of big data are made possible by the integration of state-of-the-art technology and techniques, which allow for efficient data intake, scalable processing, and sophisticated analytics.