On-Line Analytical Processing (OLAP) refers to the technologies that allow users to efficiently retrieve data from the data warehouse for decision-support purposes. Data warehouses tend to be extremely large - it is quite possible for a data warehouse to be hundreds of gigabytes to terabytes in size . Queries tend to be complex and ad hoc, often requiring computationally expensive operations such as joins and aggregation. Given this, we are interested in developing strategies for improving query processing in data warehouses by exploring the applicability of parallel processing techniques. In particular, we exploit the natural partitionability of a star schema and render it even more efficient by applying DataIndexes - a storage structure that serves both as an index as well as data and lends itself naturally to vertical partitioning of the data. Dataindexes are derived from the various special purpose access mechanisms currently supported in commercial OLAP products. Specifically, we propose a declustering strategy which incorporates both task and data partitioning and present the Parallel Star Join (PSJ) Algorithm, which provides a means to perform a star join in parallel using efficient operations involving only rowsets and projection columns. We compare the performance of the PSJ Algorithm with two parallel query processing strategies. The first is a parallel join strategy utilizing the Bitmap Join Index (BJI), arguably the state-of-the-art OLAP join structure in use today. For the second strategy we choose a well-known parallel join algorithm, namely the pipelined hash algorithm. To assist in the performance comparison, we first develop a cost model of the disk access and transmission costs for all three approaches. Performance comparisons show that the DataIndex-based approach leads to dramatically lower disk access costs than the BJI, as well as the hybrid hash approaches, in both speedup and scaleup experiments, while the hash-based approach outperforms the BJI in disk access costs. With regard to transmission overhead, our performance results show that PSJ and BJI outperform the hash-based approach. Overall, our parallel star join algorithm and dataindexes form a winning combination.