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Recent advancements in data intensive computing for science discovery are fueling a dramatic growth in use of data-intensive iterative computations. The utility computing model introduced by cloud computing combined with the rich set of cloud infrastructure services offers a very attractive environment for scientists to perform such data intensive computations. The challenges to large scale distributed computations on clouds demand new computation frameworks that are specifically tailored for cloud characteristics in order to easily and effectively harness the power of clouds. Twister4Azure is a distributed decentralized iterative MapReduce runtime for Windows Azure Cloud. It extends the familiar, easy-to-use MapReduce programming model with iterative extensions, enabling a wide array of large-scale iterative data analysis for scientific applications on Azure cloud. This paper presents the applicability of Twister4Azure with highlighted features of fault-tolerance, efficiency and simplicity. We study three data-intensive applications - two iterative scientific applications, Multi-Dimensional Scaling and KMeans Clustering, one data - intensive pleasingly parallel scientific application, BLAST+ sequence searching. Performance measurements show comparable or a factor of 2 to 4 better results than the traditional MapReduce runtimes deployed on up to 256 instances and for jobs with tens of thousands of tasks.