In-database processing: Difference between revisions

Revision as of 16:08, 31 March 2014

Revision as of 05:15, 1 April 2014

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Traditional approaches to data analysis require data to be moved out of the database into a separate analytics environment for processing, and then back to the database. ([[SPSS]] from [[IBM]] are examples of tools that still do this today). Doing the analysis in the database, where the data resides, eliminates the costs, time and security issues associated with the old approach by doing the processing in the data warehouse itself.<ref name="DBTA">{{citation|last=Das|first=Joydeep|title=Adding Competitive Muscle with In-Database Analytics|url=http://www.dbta.com/Articles/Editorial/Trends-and-Applications/Adding-Competitive-Muscle-with-In-Database-Analytics-67126.aspx|publisher=Database Trends & Applications|date=May 10, 2010}}</ref>

Though in-database capabilities were first commercially offered in the mid-1990s, as object-related database systems from vendors including IBM, [[Illustra]]/[[Informix]] (now IBM) and [[Oracle Corporation|Oracle]], the technology did not begin to catch on until the mid-2000s.<ref name="IE">{{citation|last=Grimes|first=Seth|title=In-Database Analytics: A Passing Lane for Complex Analysis|url=http://intelligent-enterprise.informationweek.com/info_centers/data_int/showArticle.jhtml;jsessionid=YH5ZICM4SKOMRQE1GHPSKH4ATMY32JVN?articleID=212500351&cid=RSSfeed_IE_News|publisher=Intelligent Enterprise|date=December 15, 2008}}</ref>

At that point, the need for in-database processing had become more pressing as the amount of data available to collect and analyze continues to grow exponentially (due largely to the rise of the Internet), from megabytes to gigabytes, terabytes and petabytes. This “[[big data]]” is one of the primary reasons it has become important to collect, process and analyze data efficiently and accurately.

Also, the speed of business has accelerated to the point where a performance gain of nanoseconds can make a difference in some industries.<ref name="DBTA">< /ref> Additionally, as more people and industries use data to answer important questions, the questions they ask become more complex, demanding more sophisticated tools and more precise results.

All of these factors in combination have created the need for in-database processing. The introduction of the [[column-oriented database]], specifically designed for analytics, data warehousing and reporting, has helped make the technology possible.

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==Vendors==

In-database processing is performed and promoted as a feature by many of the major data warehousing vendors, including [[Teradata]] (and acquired [[Aster Data Systems]]), IBM [[Netezza]], EMC [[Greenplum]], [[Sybase]], [[ParAccel]], SAS, and [[EXASOL]]. [[Fuzzy_Logix|Fuzzy Logix]] offers libraries of in-database models used for mathematical, statistical, data mining, simulation and classification modelling as well as financial models for equity, fixed income, interest rate and portfolio optimization.

==Related Technologies==

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==References==

{{Reflist|2}}

{{Database models}}