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The basis of Analytic or Decision Support Systems is the ability to process complex ad-hoc queries on large volumes of data. Processing this amount of data within a single process or thread on traditional row-oriented database is time consuming. Consequently it is beneficial Parallel Query to break down such queries into multiple sub tasks to complex the query more quickly. Additional features such as compression and partitioning are also used with Parallel Query to improve performance. As a consequence when planning analytic workloads for optimal performance you should consider the database features for in-memory and parallel execution configuration. In similarity to the HammerDB OLTP workload, HammerDB implements a fair usage of a TPC workload however the results should not be compared to official published TPC-H results in any way.