Over the past 15 years, data warehousing and OLAP technologies have matured to the point whereby they have become a cornerstone for the decision making process in organizations of all sizes. With the underlying databases growing enormously in size, parallel DBM systems have become a popular target platform. Perhaps the most ``obvious'' approach to scalable warehousing is to combine a small collection of conventional relational DBMSs into a loosely connected parallel DBMS. Such systems, however, benefit little, if at all, from advances in OLAP indexing, storage, compression, modeling, or query optimization. In the current paper, we discuss a parallel analytics server that has been designed from the ground up as a high performance OLAP query engine. Moreover, its indexing and query processing model directly exploits an OLAP-specific algebra that enables performance optimizations beyond the reach of simple relational DBMS clusters. Taken together, the server provides class-leading query performance with the scalability of shared nothing databases and, perhaps most importantly, achieves this balance with a modest physical architecture.
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