Intelligent Database Operations: Leveraging AI-Driven Observability and Predictive Maintenance in Cloud Platforms

Abstract

Databases are the backbone of digital business in modern enterprises, but this issue is still a challenge in the multi-cloud environment, as it is necessary to manage the performance, reliability, and scalability of databases in the new environment. The conventional monitoring frameworks are based on predefined thresholds and reactive troubleshooting, which usually result in a slow reaction to the issue and the case of ineffective resource utilization. This article discusses AI-based Observability and Proactive Maintenance of intelligent database processes new technology that combines telemetry analytics, machine learning, and automation to develop self-optimizing database systems. The AI-Driven Observability Model suggested incorporates both predictive analytics and real-time automation to shift away from reactive database management to being proactive. By making a comparative analysis between the managed cloud services, the methodology exhibits quantifiable benefits in the reduction of query latency, availability enhancement, and cost efficiency of operations. The framework is a critical step in the direction of autonomous operations of a database as a sub-unit of an intelligent cloud infrastructure.