International Journal of Science, Engineering and TechnologyAuthors: Melissa Wright, Eleanor Brooks, Christopher Evans, Andrew Morgan, Chaitanya Srinivas, Aneesha Raj
Abstract: Cloud migration has become a strategic priority for organizations seeking to modernize enterprise data warehouses and support scalable, high-performance analytics in increasingly data-driven business environments. Traditional on-premises data warehouse infrastructures often face challenges related to scalability, operational complexity, maintenance costs, and limited flexibility, making cloud-native platforms an attractive alternative. This paper presents a comprehensive framework for Snowflake-based modernization of enterprise data warehouses that provides a structured approach for planning, executing, and optimizing cloud migration initiatives. The proposed framework integrates migration assessment, data modeling, metadata management, extract-transform-load (ETL/ELT) modernization, security, governance, performance optimization, and continuous monitoring into a unified cloud transformation strategy. It leverages Snowflake's multi-cluster shared data architecture, elastic scalability, automatic workload management, and secure data-sharing capabilities to improve data accessibility, operational efficiency, and analytical performance. Furthermore, the framework incorporates automation, artificial intelligence-assisted migration planning, metadata-driven transformation, and data validation techniques to reduce migration risks, minimize downtime, and ensure data consistency throughout the migration lifecycle. Governance-driven controls, role-based security, regulatory compliance mechanisms, and cost optimization strategies are also integrated to support sustainable cloud operations. The proposed framework enables organizations to accelerate digital transformation, enhance enterprise analytics, improve business agility, and establish resilient cloud-native data ecosystems capable of supporting real-time decision-making and future technological innovation.
DOI: http://doi.org/10.5281/zenodo.21849308
