OPTIMIZING REAL-TIME DATA PROCESSING COSTS: A HYBRID APPROACH INTEGRATING TIERED STORAGE AND AI-DRIVEN ETL PIPELINE MANAGEMENT

Abstract

This article presents a comprehensive framework for optimizing costs in real-time data processing systems through the integration of tiered storage architecture and artificial intelligence-driven ETL pipeline management. The proposed approach combines dynamic resource allocation strategies with machine learning algorithms to achieve efficient data processing while minimizing operational expenses. By implementing an intelligent auto-scaling mechanism and leveraging AI for ETL optimization, the system demonstrates significant cost reductions across various industrial applications. The framework incorporates anomaly detection capabilities and automated parameter tuning, enabling adaptive resource management based on workload patterns. Experimental results across financial, e-commerce, and healthcare sectors validate the framework's effectiveness in maintaining processing efficiency while optimizing cloud resource utilization. This article contributes to the growing body of knowledge in cost-efficient data processing architectures and provides practical insights for organizations seeking to balance performance requirements with operational costs in cloud-based environments.