Parallel Data Loader and OLAP Operations

Constructing fact tables and performing OLAP operations are some of the most essential but expensive operations in data warehousing. OLAP operations require aggregation on many combinations of each dimension attribute. As the number of dimensions increase, it becomes very expensive to compute summary data cubes because the cost of required computations grows exponentially with the increase of number of dimensions. Query processing for these applications requires different views of data for analysis and effective decision-making. As data warehouses grow, parallel processing techniques can be applied to enable the use of larger data sets and reduce the time for analysis, thereby enabling evaluation of many more options for decisionmaking. Architecture for computing OLAP operations in parallel, using multiple processors is presented in this paper.