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Data analysis is an important functionality in cloud computing which allows a huge amount of data to be processed over very large clusters. MapReduce is recognized as a popular way to handle data in the cloud environment due to its excellent scalability and good fault tolerance. However, compared to parallel databases, the performance of MapReduce is slower when it is adopted to perform complex data analysis tasks that require the joining of multiple datasets in order to compute certain aggregates. A common concern is whether MapReduce can be improved to produce a system with both scalability and efficiency. In this paper, the authors introduce Map-Join-Reduce, a system that extends and improves MapReduce runtime framework to efficiently process complex data analysis tasks on large clusters.
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