Abstract

Cloud data migration has become a foundational pillar in enterprise digital transformation, particularly as organizations seek to leverage cloud-based analytics platforms for real-time decision-making and operational agility. This systematic review explores the underlying models, migration types, tools, and integration strategies essential for seamless data transitions across cloud environments. Drawing from empirical case studies and scholarly sources, the paper contrasts manual and automated migration techniques, assesses leading migration platforms—including AWS DMS, Azure Migrate, and Google Transfer Service—and highlights critical factors such as data consistency, latency, security, and compliance. Best practices are outlined across three key phases: pre-migration planning, governance and security alignment, and post-migration optimization. Practical implications for enterprise stakeholders are discussed, providing actionable guidance for IT leaders, data architects, and compliance officers. The paper concludes by identifying future research opportunities in AI-driven migration automation and cross-cloud orchestration. By integrating technical precision with strategic alignment, this review offers a comprehensive foundation for advancing enterprise analytics through optimized cloud data migration.