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
Distributed Data Management Architecture named RDB (for relational database) and SQLAM (for SQL Application Manager). Transparency is a key objective of DDM architecture. Without recompilation, it should be possible to redirect existing application programs to the data management services of a remote computer. For files, this was largely accomplished by DDM clients at the interface/functional level, but what about the data fields in a record? Complete transparency requires that client application programs be able to write and read fields as encoded by their local data management system, regardless of how any remote server encodes them, and that implies automatic data conversions. For example, IBM