In-database processing: Difference between revisions

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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> The concept of migrating analytics from the analytical workstation and into the Enterprise Data Warehouse was first introduced by Thomas Tileston in his presentation entitled, “Have Your Cake & Eat It Too! Accelerate Data Mining Combining SAS & Teradata” at the [[Teradata]] Partners 2005 "Experience the Possibilities" conference in Orlando, FL, September 18–22, 2005. Mr. Tileston later presented this technique globally in 2006,<ref>{{Cite web|url=http://www.itworldcanada.com/article/business-intelligence-taking-the-sting-out-of-forecasting/7193|title=Business Intelligence – Taking the sting out of forecasting &#124; IT World Canada News|date=31 October 2006}}</ref> 2007<ref>http://www2.sas.com/proceedings/forum2007/371-2007.pdf {{Bare URL PDF|date=March 2022}}</ref><ref>{{Cite web |url=http://de.saswiki.org/wiki/SAS_Global_Forum_2007 |title=SAS Global Forum 2007 – SAS-Wiki |access-date=2014-08-21 |archive-date=2014-08-21 |archive-url=https://web.archive.org/web/20140821121434/http://de.saswiki.org/wiki/SAS_Global_Forum_2007 |url-status=dead }}</ref><ref>{{Cite web |url=http://lexjansen.com/cgi-bin/sug_proceedings_pdf.php?c=SUGI&x=SGF2007 |title=Archived copy |access-date=2014-08-21 |archive-url=https://web.archive.org/web/20140822051218/http://lexjansen.com/cgi-bin/sug_proceedings_pdf.php?c=SUGI&x=SGF2007 |archive-date=2014-08-22 |url-status=dead }}</ref> and 2008.<ref>http://www.teradata.kr/teradatauniverse/PDF/Track_2/2_2_Warner_Home_Thomas_Tileston.pdf {{Bare URL PDF|date=March 2022}}</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.

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===Loading C or C++ libraries into the database process space===

With C or C++ UDF libraries that run in process, the functions are typically registered as built-in functions within the database server and called like any other built-in function in a SQL statement. Running in process allows the function to have full access to the database server’sserver's memory, parallelism and processing management capabilities. Because of this, the functions must be well-behaved so as not to negatively impact the database or the engine. This type of UDF gives the highest performance out of any method for OLAP, mathematical, statistical, univariate distributions and data mining algorithms.

===Out-of-process===

Out-of-process UDFs are typically written in C, C++ or Java. By running out of process, they do not run the same risk to the database or the engine as they run in their own process space with their own resources. Here, they wouldn’twouldn't be expected to have the same performance as an in-process UDF. They are still typically registered in the database engine and called through standard SQL, usually in a stored procedure. Out-of-process UDFs are a safe way to extend the capabilities of a database server and are an ideal way to add custom data mining libraries.

==Uses==

In-database processing makes data analysis more accessible and relevant for high-throughput, real-time applications including fraud detection, credit scoring, risk management, transaction processing, pricing and margin analysis, usage-based micro-segmenting, behavioral ad targeting and recommendation engines, such as those used by customer service organizations to determine next-best actions.<ref name=Kobelius>{{citation|last=Kobelius|first=James|title=The Power of Predictions: Case Studies in CRM Next Best Action|url=http://www.forrester.com/The+Power+Of+Predictions/fulltext/-/E-RES60094|publisher=Forrester|date=June 22, 2011|access-date=May 15, 2012|archive-date=April 13, 2012|archive-url=https://web.archive.org/web/20120413193606/http://www.forrester.com/The+Power+Of+Predictions/fulltext/-/E-RES60094|url-status=dead}}</ref>

==Vendors==

In-database processing is performed and promoted as a feature by many of the major data warehousing vendors, including [[Teradata]] (and [[Aster Data Systems]], which it acquired), IBM (with its [[Netezza]], PureData Systems, and [https://www.ibm.com/analytics/data-management/data-warehouse Db2 Warehouse] products), IEMC [[Greenplum]], [[Sybase]], [[ParAccel]], SAS, and [[EXASOL]]. Some of the products offered by these vendors, such as CWI's [[MonetDB]] or IBM's Db2 Warehouse, offer users the means to write their own functions (UDFs) or extensions (UDXs) to enhance the products' capabilities.<ref>{{cite web | url = https://www.monetdb.org/content/embedded-r-monetdb | title = Embedded R in MonetDB | date = 22 December 2014 | access-date = 22 December 2014 | archive-date = 13 November 2014 | archive-url = https://web.archive.org/web/20141113025427/https://www.monetdb.org/content/embedded-r-monetdb | url-status = dead }}</ref> [[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. [http://in-database.com In-DataBase Pioneers] collaborates with marketing and IT teams to institutionalize data mining and analytic processes inside the data warehouse for fast, reliable, and customizable consumer-behavior and predictive analytics.

==Related Technologies==

In-database processing is one of several technologies focused on improving data warehousing performance. Others include [[parallel computing]], shared everything architectures, [[shared nothing architecture]]s and [[massive parallel processing]]. It is an important step towards improving [[predictive analytics]] capabilities.<ref name="TimManns">httphttps://timmanns.blogspot.com/2009/01/isnt-in-database-processing-old-news.html "Isn't In-database processing old news yet?," "Blog by Tim Manns (Data Mining Blog)," January 8, 2009</ref>

==External links==