Sparsity in a data cube refers to many empty or zero$-$value cells within the data cube. Sparsity is significant in multidimensional datasets where combinations of dimension values may not exist for all possible dimension permutations. Managing sparsity is crucial because it impacts storage and performance. Efficient data structures and algorithms are often required to store sparse cubes compactly and to perform operations on them effectively, ensuring that resources are well$-$spent on empty cells and that operations are not unnecessarily slowed down by the need to process large volumes of non$-$existent data.