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Distributed data processing. Distributed data processing [1] (DDP) [2] was the term that IBM used for the IBM 3790 (1975) and its successor, the IBM 8100 (1979). Datamation described the 3790 in March 1979 as "less than successful." [3] [4] Distributed data processing was used by IBM to refer to two environments: IMS DB/DC; CICS/DL/I [5] [6]
Distributed Data Management Architecture (DDM) is IBM's open, published software architecture for creating, managing and accessing data on a remote computer. DDM was initially designed to support record-oriented files; it was extended to support hierarchical directories, stream-oriented files, queues, and system command processing; it was further extended to be the base of IBM's Distributed ...
The RM-ODP view model, which provides five generic and complementary viewpoints on the system and its environment.. Reference Model of Open Distributed Processing (RM-ODP) is a reference model in computer science, which provides a co-ordinating framework for the standardization of open distributed processing (ODP).
Google, one of the biggest internet companies, has created its own distributed file system, named Google File System (GFS), to meet the rapidly growing demands of Google's data processing needs, and it is used for all cloud services. GFS is a scalable distributed file system for data-intensive applications.
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.
Because distributed databases store data across multiple computers, distributed databases may improve performance at end-user worksites by allowing transactions to be processed on many machines, instead of being limited to one. [2] Two processes ensure that the distributed databases remain up-to-date and current: replication [3] and duplication.
Spark Core is the foundation of the overall project. It provides distributed task dispatching, scheduling, and basic I/O functionalities, exposed through an application programming interface (for Java, Python, Scala, .NET [16] and R) centered on the RDD abstraction (the Java API is available for other JVM languages, but is also usable for some other non-JVM languages that can connect to the ...
Data locality is a specific type of temporal locality common in signal and media processing applications where data is produced once, read once or twice later in the application, and never read again. Intermediate streams passed between kernels as well as intermediate data within kernel functions can capture this locality directly using the ...