From Legacy to Cloud: Scientific Approaches to Data Migration Challenges

Abstract

The migration of data from legacy systems to cloud environments has emerged as a cornerstone of digital transformation strategies. However, this process is fraught with challenges, ranging from schema mismatches and data inconsistency to performance bottlenecks and compliance risks. Traditional approaches to migration, rooted in deterministic ETL pipelines, often fall short when faced with the volume, velocity, and variety of modern enterprise data. This paper explores scientific approaches to overcoming these challenges, emphasizing algorithmic models, optimization techniques, and advanced validation frameworks. By leveraging machine learning, heuristic optimization, and cloud-native architectures, organizations can achieve scalable, accurate, and secure migrations. The discussion further highlights how these scientific methodologies must be embedded in enterprise strategies to ensure not only technical success but also alignment with organizational resilience and long-term scalability.