Computation Model of Data Intensive Computing with MapReduce

It becomes obvious that traditional platforms and processing paradigms can't store and process huge amounts of data. The only solution is to use specially designed ad-hoc platform/architecture based on parallelization that distributes data across large cluster of physical machines. Data Intensive Computing is a subclass of general parallel computing concept which is based on division of large amounts of data into independent parts and processing them in parallel. In the paper the alternative parallelization architectures are reviewed. MapReduce Programming model associated with distributed massive parallel processing of large amount of data is examined. The main objective of this study is to investigate conceptual fundament behind very popular data-drive computation model MapReduce.