Autonomous Data Quality Monitoring with AI Agents: Integrating ML with Cloud Warehouses and Data Lakes

Since storing data in cloud warehouses and lakes has increased, it is now important to rely on automation for keeping data accurate and trustworthy. It outlines an autonomous way of monitoring data quality by using AI agents with botnet techniques and cloud architecture. Key features of the system are that it constantly scans the data, finds unusual activities, detects any changes in the structure, and responds to any data updates without someone needing to act. Missed values, duplication, inconsistency, and noticing outliers on both structured and semi-structured data is done by using both supervised and unsupervised ML models. AI agents interact by way of a network pipeline and use reinforcement learning to pick the best solutions. easily set up Cloud Genomics in any leading cloud environment, and it works well with data warehouses as well as data lakes. Experimental evidence shows that AI-driven analysis is better at detecting errors, offering faster results, and being more adaptable than hand-made and rule-based ones. In this research, data pipelines can become more flexible and easier to manage in today's business architecture.