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 The HPCC approach also utilizes commodity clusters of hardware running the Linux operating system. Custom system software and middleware components were developed and layered on the base Linux operating system to provide the execution environment and distributed filesystem support required for data-intensive computing. LexisNexis also implemented a new high-level language for data-intensive computing. The ECL programming language is a high-level, declarative, data-centric, implicitly parallel language that allows the programmer to define what the data processing result should be and the dataflows and transformations that are necessary to achieve the result. The ECL language includes extensive capabilities for data definition, filtering,