Amazon Web Services, USA.
World Journal of Advanced Engineering Technology and Sciences, 2025, 15(03), 1241–1248
Article DOI: 10.30574/wjaets.2025.15.3.1061
Received on 02 May 2025; revised on 10 June 2025; accepted on 12 June 2025
This article presents a comprehensive framework for automating the migration of legacy data systems to cloud platforms through an AI-driven approach. It addresses the critical balance between risk mitigation, cost management, and operational continuity throughout the modernization journey. By leveraging advanced machine learning algorithms for schema discovery, automated code generation, performance optimization, and continuous validation, organizations can significantly reduce manual efforts while accelerating migration timelines. The framework incorporates intelligent scanning of diverse source systems, automated schema mapping to cloud warehouses, machine learning-based performance tuning, robust validation mechanisms, and infrastructure provisioning through Infrastructure as Code. This systematic approach enables enterprises to confidently transition from legacy platforms to cloud-native analytics ecosystems while maintaining data fidelity and minimizing business disruption.
Data Modernization; AI-Driven Migration; Schema Automation; Cloud Data Warehousing; ETL Optimization
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Rakshit Khare. Accelerating digital transformation: AI-driven frameworks for legacy-to-cloud data modernization. World Journal of Advanced Engineering Technology and Sciences, 2025, 15(03), 1241-1248. Article DOI: 10.30574/wjaets.2025.15.3.1061.