Observational analysis reveals that leveraging GenAI can optimize NoSQL database migrations, highlighting reduced risks.
Migrating from on-premises relational databases to cloud based NoSQL database [1] represents a fundamental shift in data architecture [2]. This transition involves moving from ACID-compliant [4], structured data models to eventually consistent, flexible schema designs. Organizations typically pursue this migration to achieve better scalability, reduce operational overhead, and leverage cloud-native capabilities. The process requires careful planning as it involves not just data movement but also application refactoring [5][20]. Modern database migrations can benefit significantly from GenAI [3] capabilities that can analyze source database patterns, predict optimal NoSQL targets, and automate complex decision-making processes. GenAI transforms traditional manual migration planning into intelligent, data-driven recommendations that reduce risks and improve outcomes. AI-powered analysis can examine years of query logs, schema evolution patterns, and performance metrics to recommend the most suitable NoSQL database type and architecture design.
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Anil Malakar (2025) studied this question.
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