International Journal of Science, Engineering and TechnologyAuthors: Ajay Kumar Kota
Abstract: Dimensional modeling has long been the foundation of Business Intelligence (BI), providing a structured framework for organizing enterprise data into facts and dimensions that enable consistent reporting and analysis. However, the exponential growth of data volumes, increasing demands for real-time analytics, and heightened regulatory pressures have exposed the limitations of traditional dimensional models. This article reimagines dimensional modeling in the context of modern enterprise BI environments by focusing on two critical priorities: performance optimization and security enforcement. It first revisits the foundations of dimensional modeling, examining the strengths and challenges of star and snowflake schemas in large-scale deployments. The discussion then explores advanced performance optimization techniques such as fact table partitioning, indexing, surrogate keys, and pre-aggregated fact tables, illustrating how these approaches reduce query latency and improve dashboard responsiveness. The article further emphasizes the growing importance of data security, particularly in regulated industries, and highlights the role of Section Access in enabling fine-grained, role-based control over data visibility.
DOI: http://doi.org/10.5281/zenodo.16991809
