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Keywords:

Relational database migration, Cloud-based NoSQL, ACID compliance, Flexible schema, Data architecture transformation ,Scalability ,Operational overhead reduction, Cloud-native capabilities, Automated database migration, GenAI (Generative AI) ,Schema analysis ,Query pattern analysis ,Data-driven decision-making ,NoSQL database selection ,Migration planning automation ,Performance optimization ,Risk mitigation, Intelligent migration tools

Data-Driven Migration Strategies: Leveraging GenAI for Relational to NoSQL Cloud Database Migrations

Authors

Anil Malakar1

Abstract

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. 

Article Details

Published

2025-09-28

Section

Articles

License

Copyright (c) 2025 International Journal of Engineering and Computer Science Creative Commons License

This work is licensed under a Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International License.

How to Cite

Data-Driven Migration Strategies: Leveraging GenAI for Relational to NoSQL Cloud Database Migrations. (2025). International Journal of Engineering and Computer Science, 14(09), 27742-27747. https://doi.org/10.18535/ijecs.v14i09.5266