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
Server-side access to data in the server's database. Alternatively, an attacker might break into a server system using vulnerabilities in the underlying operating system and then be able to access database and other files in the same manner as authorized administrators of the server. In the case of distributed computing projects such as SETI@home and the Great Internet Mersenne Prime Search, while the bulk of the operations occur on the client side, the servers are responsible for coordinating the clients, sending them data to analyze, receiving and storing results, providing reporting functionality to project administrators, etc. In the case of an