Leveraging AI for Intelligent Data Management in Multi-Cloud Database Architectures
Abstract
As organizations increasingly adopt multi-cloud architectures to leverage the benefits of diverse cloud providers, managing data across multiple platforms has become a complex challenge. Traditional data management techniques often struggle to maintain consistency, performance, and security across these distributed environments. This paper explores the integration of Artificial Intelligence (AI) in multi-cloud database architectures to optimize data management processes. We examine how AI-driven technologies such as machine learning, predictive analytics, and automation can improve data consistency, optimize resource allocation, enhance security, and streamline performance monitoring. Additionally, the paper discusses the role of AI in real-time data analytics, anomaly detection, and self-healing systems, which contribute to intelligent decision-making and proactive issue resolution. By leveraging AI, organizations can achieve more efficient, resilient, and scalable multi-cloud database architectures, reducing operational costs and improving overall system performance. The paper concludes by outlining the future potential of AI in further transforming multi-cloud data management.
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