Scalable and Intelligent Big Data Analytics Framework (SIBDAF) for Cloud Environments

In today’s data-driven landscape, the convergence of big data analytics and cloud computing has become imperative to process and extract insights from massive datasets. This paper introduces a Scalable and Intelligent Big Data Analytics Framework (SIBDAF) designed to harness the power of cloud environments for advanced data processing. The framework efficiently handles large-scale data and integrates intelligent techniques for valuable insights. The SIBDAF architecture includes a robust ecosystem for data storage, scalable processing engines, and intelligent analytics modules. Leveraging the cloud’s elastic nature, it achieves seamless scalability for processing data at unprecedented scales. Machine learning and artificial intelligence techniques are integrated for intelligent analytics, including predictive modeling, clustering, and classification. A comprehensive evaluation with diverse datasets and workloads demonstrates significant improvements in data processing times and resource utilization compared to conventional approaches. Real-world use cases showcase the framework’s versatility across domains such as business, health care, and scientific research. This research introduces an innovative framework that addresses big data analytics’ scalability demands and empowers users to extract deeper insights. By bridging the gap between scalable infrastructure and advanced analytics, the Scalable and Intelligent Big Data Analytics Framework sets a new precedent for cloud-based data processing, enhancing decision-making in data-intensive applications.