Designing High-Performance OLAP Cubes for Advanced Analytical Decision-Making

Abstract

The growing size of enterprise data requires analytical systems that can facilitate effective decision-making. Online Analytical Processing (OLAP) cubes have the advantage of allowing multidimensional analysis but have challenges in scalability and latency with handling heterogeneous data. The framework to be proposed in this paper is based on developing high-performance OLAP cubes, combining dimensional modeling optimization, scalable structuring, and contemporary storage approaches. However, the strategy enhances the responsiveness and decision capability of analytics by matching cube architecture and performance evaluation metrics. The article offers insights into the balance between scalability, usability and computational efficiency in the modern analytics setting.