Resilient Dataflow Intelligence (RDI): A Proactive AI Framework for Anomaly Anticipation in Cloud Databases

Cloud databases are becoming more essential to enterprise applications, but they are still susceptible to unreliable failures, performance outbursts, and corruption of data. The conventional state-of-the-art methods of monitoring are based on reactive detection of anomalies, where the problems are recognized when they have already happened. In this paper, a new framework named Resilient Dataflow Intelligence (RDI) will be presented, which is a multi-layer AI-driven framework aimed at proactively detecting anomalies in cloud database systems. RDI combines real-time stream processing, ensemble machine learning models, and predictive analytics to identify early indicators of anomalies, which disrupt operations. Tests on three leading cloud providers, AWS RDS, Google Cloud SQL, and Microsoft Azure Database, show prediction accuracy of 93.2, a downtime of 48 percent, and inference in real time of less than 15 milliseconds. E-commerce, healthcare, and financial services. Case studies prove the increases in database reliability, operational efficiency, and cost optimization, and the average ROI is realized within less than 10 months. The study confirms RDI as a viable and scalable instrument to change the paradigm of database monitoring and transition database monitoring tools to be more proactive rather than reactive.