Multidimensional Data Cube: Most OLAP products are developed based on a structure where the cube is patterned as a multidimensional array. These multidimensional OLAP (MOLAP) products usually offers improved performance when compared to other approaches mainly because they can be indexed directly into the structure of the data cube to gather subsets of data. When the number of dimensions is greater, the cube becomes sparser. That means that several cells that represent particular attribute combinations will not contain any aggregated data. This in turn boosts the storage requirements, which may reach undesirable levels at times, making the MOLAP solution untenable for huge data sets with many dimensions. Compression techniques might help; however, their use can damage the natural indexing of MOLAP.
Relational OLAP: Relational OLAP make use of the relational database model. The ROLAP data cube is employed as a bunch of relational tables (approximately twice as many as the quantity of dimensions) compared to a multidimensional array. Each one of these tables, known as a cuboid, signifies a specific view.
