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
Computer data storage data storage on a medium or a device that is not under the control of a processing unit. The medium is recorded, usually in a secondary or tertiary storage device, and then physically removed or disconnected. It must be inserted or connected by a human operator before a computer can access it again. Unlike tertiary storage, it cannot be accessed without human interaction. Off-line storage is used to transfer information, since the detached medium can be easily physically transported. Additionally, in case a disaster, for example a fire, destroys the original data, a medium in a remote location will probably