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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

    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]

  4. Data-intensive computing - Wikipedia

    en.wikipedia.org/wiki/Data-intensive_computing

    These additional subprojects provide enhanced application processing capabilities to the base Hadoop implementation and currently include Avro, Pig, HBase, ZooKeeper, Hive, and Chukwa. The Hadoop MapReduce architecture is functionally similar to the Google implementation except that the base programming language for Hadoop is Java instead of ...

  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. Distributed file system for cloud - Wikipedia

    en.wikipedia.org/wiki/Distributed_file_system...

    Modern data centers must support large, heterogenous environments, consisting of large numbers of computers of varying capacities. Cloud computing coordinates the operation of all such systems, with techniques such as data center networking (DCN), the MapReduce framework, which supports data-intensive computing applications in parallel and distributed systems, and virtualization techniques ...

  7. 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 ...

  8. Lambda architecture - Wikipedia

    en.wikipedia.org/wiki/Lambda_architecture

    The two view outputs may be joined before presentation. The rise of lambda architecture is correlated with the growth of big data, real-time analytics, and the drive to mitigate the latencies of map-reduce. [1] Lambda architecture depends on a data model with an append-only, immutable data source that serves as a system of record.

  9. Wikipedia:Database download - Wikipedia

    en.wikipedia.org/wiki/Wikipedia:Database_download

    You can do Hadoop MapReduce queries on the current database dump, but you will need an extension to the InputRecordFormat to have each <page> </page> be a single mapper input. A working set of java methods (jobControl, mapper, reducer, and XmlInputRecordFormat) is available at Hadoop on the Wikipedia