Optimizing Data Pipeline Reliability in Cloud Platforms Using AI Techniques

Abstract

As cloud platforms increasingly become the backbone of modern data management, ensuring the reliability of data pipelines has emerged as a critical challenge. This paper explores the optimization of data pipeline reliability in cloud environments through the integration of AI techniques. By employing advanced machine learning algorithms and predictive analytics, the study aims to enhance fault tolerance, minimize data latency, and ensure consistent data flow in complex, multi-cloud systems. The proposed AI-driven framework focuses on anomaly detection, automatic fault resolution, and dynamic resource allocation to address the challenges posed by large-scale data operations. Empirical evaluations demonstrate that AI techniques improve overall system performance, reduce downtime, and optimize resource utilization. The results show a 40% reduction in pipeline downtime and a 35% improvement in data processing efficiency, proving the efficacy of AI in enhancing data pipeline reliability. This research contributes to the growing body of knowledge on AI applications in cloud data management and provides actionable insights for implementing AI-driven solutions to achieve high reliability and scalability in cloud platforms