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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 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 ...

  4. Apache Hive - Wikipedia

    en.wikipedia.org/wiki/Apache_Hive

    The query CREATE TABLE word_counts AS SELECT word, count(1) AS count creates a table called word_counts with two columns: word and count. This query draws its input from the inner query (SELECT explode (split (line, '\s')) AS word FROM docs) temp ". This query serves to split the input words into different rows of a temporary table aliased as temp.

  5. Big data - Wikipedia

    en.wikipedia.org/wiki/Big_data

    In 2004, Google published a paper on a process called MapReduce that uses a similar architecture. 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 ...

  6. Template:Word count - Wikipedia

    en.wikipedia.org/wiki/Template:Word_count

    2.1 Parameter trim= example showing all text (trim=no) or truncated text (trim=yes) ... It serves as a basic word count function in areas where word count is ...

  7. MongoDB - Wikipedia

    en.wikipedia.org/wiki/MongoDB

    MongoDB provides three ways to perform aggregation: the aggregation pipeline, the map-reduce function and single-purpose aggregation methods. [40] Map-reduce can be used for batch processing of data and aggregation operations. However, according to MongoDB's documentation, the aggregation pipeline provides better performance for most ...

  8. Apache Spark - Wikipedia

    en.wikipedia.org/wiki/Apache_Spark

    A typical example of RDD-centric functional programming is the following Scala program that computes the frequencies of all words occurring in a set of text files and prints the most common ones. Each map , flatMap (a variant of map ) and reduceByKey takes an anonymous function that performs a simple operation on a single data item (or a pair ...

  9. Module:Word count - Wikipedia

    en.wikipedia.org/wiki/Module:Word_count

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