A Hybrid Cloud-Based Predictive Analytics Framework: Balancing Scalability, Cost Efficiency, and Data Security in Big Data Processing

Abstract

The exponential growth of big data presents substantial challenges for organizations that need to process and analyze amounts of real-time and batch data efficiently, while adhering to stringent data security and regulatory requirements. Traditional on-premises infrastructures, though secure, often lack the scalability and flexibility needed to manage such high-volume data, whereas fully cloud-based solutions raise concerns about data privacy and compliance. To address these issues, this paper proposes a novel hybrid cloud-based big-data framework designed specifically for predictive analytics. The framework integrates scalability, elasticity, and cost-efficiency of cloud platforms with  security and control provided by the on-premises infrastructure. By dynamically partitioning workloads based on data sensitivity and processing requirements, the system ensures optimal resource allocation and performance across diverse data-processing tasks. The proposed framework was evaluated across several key performance metrics, demonstrating its ability to handle both real-time streaming data and batch data processing effectively. The experimental results indicate that the system achieves high scalability, processing 8,000 data units per second while maintaining a low latency of 30 ms for real-time analytics. In terms of cost efficiency, the framework significantly reduces expenses, with a cost of $200 per terabyte of processed data compared with traditional solutions. Furthermore, the framework enhances predictive accuracy, with a mean squared error (MSE) of 0.03, outperforming both on-premises and fully cloud-based systems. The flexibility of the architecture allows for the secure processing of sensitive data on-premises to meet regulatory compliance (e.g., GDPR, HIPAA), whereas non-sensitive data are processed in the cloud, leveraging the cloud’s elastic computational resources. This framework addresses the key limitations of the existing data infrastructure, providing a balanced solution that optimizes performance, security, and cost. However, challenges, such as the overhead introduced by data transfers between the cloud and on-premises systems, as well as the complexity of managing a hybrid environment, are acknowledged. Future research will focus on minimizing these challenges through enhanced data synchronization methods and intelligent workload orchestration. Overall, this study contributes to the growing field of hybrid cloud architectures for big data analytics, offering a scalable and secure solution that meets the demands of modern data-driven organizations.