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The combination of low-cost sensors, low-cost commodity computing, and the Internet is enabling a new era of data-intensive science. The dramatic increase in this data availability has created a new challenge for scientists: how to process the data. Scientists today are envisioning scientific computations on large scale data but are having difficulty designing software architectures to accommodate the large volume of the often heterogeneous and inconsistent data. In this paper, the authors introduce a particular instance of this challenge, and present the design and implementation of a MODIS satellite data reprojection and reduction pipeline in the Windows Azure cloud computing platform. This cloud-based pipeline is designed with a goal of hiding data complexities and subsequent data processing and transformation from end users.
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