Whitepaper - In-Memory Analytics

Discussion in 'Java' started by JTP PR, Apr 26, 2010.

  1. JTP PR

    JTP PR Guest

    Whitepaper - In-Memory Analytics


    Yellowfin has released its latest Business Intelligence Whitepaper,
    this time on "In-memory Analytics"

    * No signup or email handover required, just click the link or cut/
    past into your browser.

    http://yellowfin.com.au/Document.i4?DocumentId=104879

    Feel free to give us any feedback

    Extract

    As the name suggests, the key difference between conventional BI tools
    and in-memory products is that the former query data on disk while the
    latter query data in random access memory (RAM).
    When a user runs a query against a typical data warehouse, for
    example, the query normally goes to a database that reads the
    information from multiple tables stored on a server’s hard disk.

    With a server-based in-memory database, all information is initially
    loaded into memory. Users then query and interact with the data loaded
    into the machine’s memory. Accessing data in-memory means it is
    literally “turbo charged” as opposed to accessing that same data from
    disk.
    This is the real advantage of in-memory analysis.

    In-memory BI may sound like caching, a common approach to speeding
    query performance, but in-memory databases do not suffer from the same
    limitations. Caches are typically subsets of data, stored on and
    retrieved from disk (though some may load into RAM).
    The key difference is that the cached data is usually predefined and
    very specific, often to an individual query; but with an in-memory
    database, the data available for analysis is potentially as large as
    an entire data mart.
    JTP PR, Apr 26, 2010
    #1
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