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2. Data-intensive computing - Wikipedia

   en.wikipedia.org/wiki/Data-intensive_computing

   Computer system architectures which can support data parallel applications were promoted in the early 2000s for large-scale data processing requirements of data-intensive computing. [12] Data-parallelism applied computation independently to each data item of a set of data, which allows the degree of parallelism to be scaled with the volume of data.

3. MapReduce - Wikipedia

   en.wikipedia.org/wiki/MapReduce

   MapReduce is a programming model and an associated implementation for processing and generating big data sets with a parallel and distributed algorithm on a cluster. [1] [2] [3]A MapReduce program is composed of a map procedure, which performs filtering and sorting (such as sorting students by first name into queues, one queue for each name), and a reduce method, which performs a summary ...

4. Apache Hadoop - Wikipedia

   en.wikipedia.org/wiki/Apache_Hadoop

   Apache Hadoop (/ h ə ˈ d uː p /) is a collection of open-source software utilities for reliable, scalable, distributed computing.It provides a software framework for distributed storage and processing of big data using the MapReduce programming model.

5. HPCC - Wikipedia

   en.wikipedia.org/wiki/HPCC

   The second of the parallel data processing platforms is called Roxie and functions as a rapid data delivery engine. This platform is designed as an online high-performance structured query and analysis platform or data warehouse delivering the parallel data access processing requirements of online applications through Web services interfaces ...

6. Electronic data processing - Wikipedia

   en.wikipedia.org/wiki/Electronic_data_processing

   Data processing facilities became available to smaller organizations in the form of the computer services bureau. These offered processing of specific applications e.g. payroll and were often a prelude to the purchase of customers' own computers.

7. Data reduction - Wikipedia

   en.wikipedia.org/wiki/Data_reduction

   Data reduction is the transformation of numerical or alphabetical digital information derived empirically or experimentally into a corrected, ordered, and simplified form. . The purpose of data reduction can be two-fold: reduce the number of data records by eliminating invalid data or produce summary data and statistics at different aggregation levels for various applications