AI-Powered Cloud Database-as-a-Service

Abstract - Cloud-based applications increasingly rely on multiple database systems to handle diverse data models and workloads, yet managing these heterogeneous environments remains complex and resource-intensive. Traditional Database-as-a-Service platforms often introduce vendor lock-in, limited flexibility, and high costs, restricting their suitability for academic and research use. To address these challenges, this research proposes an open-source, AI-powered Cloud Database-as-a-Service platform that unifies the management of SQL, NoSQL, and in-memory databases using Kubernetes-based container orchestration. The system integrates AI-driven natural language assistance for schema generation and query formulation, along with real-time monitoring using Prometheus and Grafana. By combining automation, intelligent interaction, and cost-effective deployment, the platform aims to improve accessibility, efficiency, and scalability in cloud-native database management. Key Words: Database-as-a-Service (DBaaS); Cloud Computing; Kubernetes Orchestration; Container Management; Artificial Intelligence (AI); Natural Language Processing (NLP); Multi-Database Systems; Microservices Architecture; Performance Monitoring; Prometheus; Grafana; Real-Time Analytics; Open-Source Platform