The performance of the MapReduce-based Cloud data warehouses mainly depends on the virtual hardware resources allocated. Most of the time, the resources are values selected/given by the Cloud service providers. However, setting the right virtual resources in accordance with the workload demands of a query, such as the number of CPUs, the size of RAM, and the network bandwidth, will improve the response time when querying large data on an optimized system. In this study, we carried out a set of experiments with a well-known Mapreduce SQL-translator, Hadoop Hive, on benchmark decision support the TPC benchmark (TPC-H) database in order to analyze the performance sensitivity of the queries under different virtual resource settings. Our results provide valuable hints for the decision makers who design efficient MapReduce-based data warehouses on the Cloud.