Exploring The Integration Of Machine Learning For Streamlined Database Administration: Current Trends And Future Directions

Abstract

The Role Of Database Administrators (DBAs) Has Traditionally Been Rooted In Manual Operations Such As Backups, Performance Tuning, And Anomaly Detection. However, As Data Complexity Grows, Manual Management Becomes Inefficient, Increasing Downtime And Operational Costs. The Integration Of Machine Learning (ML) Into Database Administration Provides Automated Solutions For Predictive Maintenance, Intelligent Indexing, Real-time Anomaly Detection, And Self-healing Capabilities. This Paper Explores How AI-driven Automation Enhances Traditional DBA Functions, Drawing Insights From Multiple Research Papers. The Study Highlights Advancements In AI-powered Database Tools Such As DBSitter, AI-driven Indexing, And Self-optimizing Systems, Demonstrating How These Innovations Reduce Human Intervention While Improving Efficiency, Security, And System Resilience.

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