Abstract: Essbase-driven multidimensional Data warehouses have integrated themselves into the systems of enterprise decision-making and have the possibility of dealing with massive amounts of data while providing analytical values. However, the management and the tuning of these data cubes to serve the given aim and perform well present many difficulties. This article takes a closer look into issues relating to data cube management and performance tuning in systems driven by Essbase. A number of underlying concepts are explained, including cube structuring, indexing, calculation scripts, data loading and partitioning. In addition, we describe the consideration of various tuning techniques such as dimension optimization, management of aggregate storage, use of cache and parallelism in an attempt to fine-tune the cube. This work uses real-world case studies and performance evaluation; it provides useful information on enhancing the quality of responses to queries, decreasing processing loads, and increasing the scalability of enterprise DWs. Else additionally it does consider the key innovations such as hybrid aggregation as well as dynamic calculations. The approach for testing consists of a blend of business response percentages and/or qualitative assessments derived from installations. At the end of the chapter, the reader will understand how to work with Essbase data cubes for optimal business performance management.

Keywords: Essbase, Data cube management, Multidimensional data warehouse, Performance tuning, Dimension structuring, Aggregate storage, Query performance, Partitioning, Dynamic calculations.

| DOI: 10.17148/IARJSET.2024.11912