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  2. 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 ...

  3. 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.

  4. RCFile - Wikipedia

    en.wikipedia.org/wiki/RCFile

    Within database management systems, the record columnar file [1] or RCFile is a data placement structure that determines how to store relational tables on computer clusters. It is designed for systems using the MapReduce framework. The RCFile structure includes a data storage format, data compression approach, and optimization techniques for ...

  5. Bigtable - Wikipedia

    en.wikipedia.org/wiki/Bigtable

    Bigtable development began in 2004. [1] It is now used by a number of Google applications, such as Google Analytics, [2] web indexing, [3] MapReduce, which is often used for generating and modifying data stored in Bigtable, [4] Google Maps, [5] Google Books search, "My Search History", Google Earth, Blogger.com, Google Code hosting, YouTube, [6] and Gmail. [7]

  6. Apache Pig - Wikipedia

    en.wikipedia.org/wiki/Apache_Pig

    Pig Latin abstracts the programming from the Java MapReduce idiom into a notation which makes MapReduce programming high level, similar to that of SQL for relational database management systems. Pig Latin can be extended using user-defined functions (UDFs) which the user can write in Java , Python , JavaScript , Ruby or Groovy [ 3 ] and then ...

  7. Hyperscale computing - Wikipedia

    en.wikipedia.org/wiki/Hyperscale_computing

    In computing, hyperscale is the ability of an architecture to scale appropriately as increased demand is added to the system. This typically involves the ability to seamlessly provide and add compute, memory, networking, and storage resources to a given node or set of nodes that make up a larger computing, distributed computing, or grid computing environment.

  8. Big data - Wikipedia

    en.wikipedia.org/wiki/Big_data

    Big data "size" is a constantly moving target; as of 2012 ranging from a few dozen terabytes to many zettabytes of data. [26] Big data requires a set of techniques and technologies with new forms of integration to reveal insights from data-sets that are diverse, complex, and of a massive scale. [27]

  9. Data mapping - Wikipedia

    en.wikipedia.org/wiki/Data_mapping

    In computing and data management, data mapping is the process of creating data element mappings between two distinct data models. Data mapping is used as a first step for a wide variety of data integration tasks, including: [1] Data transformation or data mediation between a data source and a destination