Ai-Powered Error Detection And Resolution In Cloud Database Migration Using AWS Database Migration Service And Schema Conversion Tool

Abstract Cloud database migration is a recent and important migration for modern enterprises. It has many difficultparts, including the detection and resolution of annotated data and schema errors. However, traditional methods only focus on rule-based validation. This approach neglects subtle anomalies in customer data and fails to support multiple data structures. Our platform combines machine learning algorithms with Amazon Web Services (AWS) Database Migration Service (DMS) and Schema Conversion Tool (SCT) to recognize mismatches, missing elements, and transformation errors in real time. We trained and tested our product against diverse heterogeneous database environments. Our results demonstrate an improvement in detecting or resolving the detected errors and solving any transformation issues faster, which also improves the efficiency and scalability of the migration workflow. Our work sheds light on how artificial intelligence can automate and optimize critical processes in cloud adoption strategies, making cloud migrations stronger, less costly, and future-proof. By developing an intelligent migration system in cloud infrastructure, we demonstratepossibilities for further research and development in intelligent migration systems.