Optimising Data Modeling Approaches for Scalable Data Warehousing Systems
Srinivas Murri
Abstract
Data warehousing has become a cornerstone of modern decision-support systems, enabling organizations to consolidate, store, and analyze large volumes of data for informed decision-making. With the exponential growth of data and increasing complexity in data sources, traditional approaches to data modeling and warehousing face challenges in scalability, real-time processing, and integration of diverse data types. This paper provides a comprehensive overview of data warehousing, highlighting its key components, traditional and modern approaches, and the evolving challenges in handling big data's volume, variety, and velocity. It emphasizes the importance of data modeling techniques, such as normalization, denormalization, and schema design strategies, to enhance scalability and performance. Furthermore, the paper identifies the critical need for innovative solutions to optimize data modeling for modern data warehouses, ensuring robust, adaptable, and efficient systems capable of supporting advanced analytics in diverse business environments.