Scaling Data Warehousing Solutions for Global Enterprise Needs

Abstract

As enterprises expand globally, the need for scalable and efficient data warehousing solutions becomes critical. This study explores strategies to scale data warehousing systems, ensuring optimal performance, reliability, and cost-efficiency across geographically distributed environments. The research evaluates architectural frameworks, cloud-native technologies, and big data integration methodologies to address scalability challenges. Empirical results demonstrate the potential of hybrid cloud architectures and distributed data processing frameworks in achieving desired scalability levels. The findings are aimed at aiding enterprises in designing robust data warehousing systems that align with global operational requirements.