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As the market demand for analyzing data sets of increasing variety and scale continues to explode, the software options for performing this analysis are beginning to proliferate. No fewer than a dozen companies have launched in the past few years that sell parallel database products to meet this market demand. At the same time, MapReduce-based options, such as the open source Hadoop framework are becoming increasingly popular, and there have been a plethora of research publications in the past two years that demonstrate how MapReduce can be used to accelerate and scale various data analysis tasks. Both parallel databases and MapReduce-based options have strengths and weaknesses that a practitioner must be aware of before selecting an analytical data management platform. In this talk, I describe some experiences in using these systems, and the advantages and disadvantages of the popular implementations of these systems. I then discuss a hybrid system that we are building at Yale University, called HadoopDB, that attempts to combine the advantages of both types of platforms. Finally, I discuss our experience in using HadoopDB for both traditional decision support workloads (i.e., TPC-H) and also scientific data management (analyzing the Uniprot protein sequence, function, and annotation data).