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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. Execution model - Wikipedia

    en.wikipedia.org/wiki/Execution_model

    Execution models can also exist independently from programming languages, examples of which would be the POSIX Threads library, and Hadoop's Map-Reduce programming model. The implementation of an execution model can be via compiler, or interpreter, and often includes a runtime system.

  4. Apache Hadoop - Wikipedia

    en.wikipedia.org/wiki/Apache_Hadoop

    Theoretically, Hadoop could be used for any workload that is batch-oriented rather than real-time, is very data-intensive, and benefits from parallel processing. It can also be used to complement a real-time system, such as lambda architecture, Apache Storm, Flink, and Spark Streaming. [59] Commercial applications of Hadoop include: [60]

  5. Apache Pig - Wikipedia

    en.wikipedia.org/wiki/Apache_Pig

    Apache Pig [1] is a high-level platform for creating programs that run on Apache Hadoop. The language for this platform is called Pig Latin. [1] Pig can execute its Hadoop jobs in MapReduce, Apache Tez, or Apache Spark. [2]

  6. Programming model - Wikipedia

    en.wikipedia.org/wiki/Programming_model

    An example is Spark where Java is the base language, and Spark is the programming model. Execution may be based on what appear to be library calls. Other examples include the POSIX Threads library and Hadoop's MapReduce. [1] In both cases, the execution model of the programming model is different from that of the base language in which the code ...

  7. Big data - Wikipedia

    en.wikipedia.org/wiki/Big_data

    The MapReduce concept provides a parallel processing model, and an associated implementation was released to process huge amounts of data. With MapReduce, queries are split and distributed across parallel nodes and processed in parallel (the "map" step). The results are then gathered and delivered (the "reduce" step).

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  9. Apache HBase - Wikipedia

    en.wikipedia.org/wiki/Apache_HBase

    Tables in HBase can serve as the input and output for MapReduce jobs run in Hadoop, and may be accessed through the Java API but also through REST, Avro or Thrift gateway APIs. HBase is a wide-column store and has been widely adopted because of its lineage with Hadoop and HDFS. HBase runs on top of HDFS and is well-suited for fast read and ...