Cloud computing is a type of parallel distributed computing system that has become a frequently used computer application. Increasingly larger scale applications are generating an unprecedented amount of data. However, the increasing gap between computation and I/O capacity on High End Computing machines makes a severe bottleneck for data analysis. Big data is an emerging paradigm applied to data sets whose size or complexity is beyond the ability of commonly used computer software and hardware tools. Such data sets are often from various sources (Variety) yet unstructured such as social media, sensors, scientific applications, surveillance, video and image archives, Internet texts and documents, Internet search indexing, medical records, business transactions and web logs; and are of large size (Volume) with fast data in/out (Velocity). More importantly, big data has to be of high value (Value) and establish trust in it for business decision making (Veracity). To handle the dynamic nature of big data successfully, architectures, networks, management, mining and analysis algorithms should be scalable and extendable to accommodate the varying needs of the applications. In this paper we propose a big data solution through cloud computing by using divide and conquer technique in parallel distribution system.
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