International Journal of Science, Engineering and TechnologyAuthors: Ajay Kumar Kota
Abstract: In today’s data-driven enterprise environments, business intelligence (BI) initiatives increasingly require integration of data from multiple heterogeneous sources to provide comprehensive, actionable insights. This article explores best practices for building scalable and efficient multi-source data models using Power BI and SQL Server. It covers the end-to-end process—from selecting and preparing diverse data sources to designing optimized data models with appropriate joins, relationships, and performance tuning strategies. Special emphasis is placed on modeling techniques such as star schema design, surrogate keys, aggregation tables, and the effective use of SQL queries within Power BI. Additionally, the article delves into advanced relationship management, cross-source linking, and query folding to ensure maintainability and refresh efficiency. A real-world case study from the pharmaceutical industry illustrates how these methods lead to meaningful executive dashboards, consolidating clinical, sales, and CRM data into a single analytical view. The article concludes with governance guidelines to manage access, version control, and metadata.
