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It is very important to evaluate the MapReducebased frameworks for scientific data processing applications. Scientists need a low-cost, scalable, easy-to-use and faulttolerance platform for large volume data processing eagerly. This paper presents an implementation of a scientific data management benchmark, SSDB, on Hive, a MapReduce-based data warehouse. A complete strategy of migrating SSDB to Hive is described in detail including query HQL implementation, data partition schema anddoi:10.1109/bigdata.2013.6691696 dblp:conf/bigdataconf/LiuLLL13 fatcat:mvgsipp2dbeujh6r3t3caexzca