Data-intensive computing this approach is considered suitable for data-intensive computing and problems which are “embarrassingly parallel”, i.e. where it is relatively easy to separate the problem into a number of parallel tasks and there is no dependency or communication required between the tasks other than overall management of the tasks. These types of data processing problems are inherently adaptable to various forms of distributed computing including clusters, data grids, and cloud computing. Several common characteristics of data-intensive computing systems distinguish them from other forms of computing: A variety of system architectures have been implemented for data-intensive computing and large-scale data analysis applications
Data-intensive computing information exists in unstructured form with increased data processing requirements compared to structured information. The storing, managing, accessing, and processing of this vast amount of data represents a fundamental need and an immense challenge in order to satisfy needs to search, analyze, mine, and visualize this data as information. Data-intensive computing is intended to address this need. Parallel processing approaches can be generally classified as either "compute-intensive", or "data-intensive". Compute-intensive is used to describe application programs that are compute bound. Such applications devote most of their execution time to computational requirements as opposed to I/O, and typically require small volumes