In-database processing - Revision history

InternetArchiveBot: Rescuing 1 sources and tagging 0 as dead.) #IABot (v2.0.9.5) (Pancho507 - 22063

2024-12-11T23:12:46Z

Rescuing 1 sources and tagging 0 as dead.) #IABot (v2.0.9.5) (Pancho507 - 22063

← Previous revision		Revision as of 23:12, 11 December 2024
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	==Uses==		==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}}</ref>		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==		==Vendors==

Onel5969: clean up, typo(s) fixed: wouldn’t → wouldn't, ’s → 's

2023-07-29T19:09:38Z

clean up, typo(s) fixed: wouldn’t → wouldn't, ’s → 's

← Previous revision		Revision as of 19:09, 29 July 2023
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	===Loading C or C++ libraries into the database process space===		===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’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.		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'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===
	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’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.		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'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==		==Uses==

InternetArchiveBot: Rescuing 1 sources and tagging 0 as dead.) #IABot (v2.0.9.3

2023-01-20T05:05:00Z

Rescuing 1 sources and tagging 0 as dead.) #IABot (v2.0.9.3

← Previous revision		Revision as of 05:05, 20 January 2023
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	==Vendors==		==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}}</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.		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==		==Related Technologies==

Citation bot: Alter: title. | Use this bot. Report bugs. | Suggested by BrownHairedGirl | #UCB_webform 2380/3834

2022-08-23T09:53:38Z

Alter: title. | Use this bot. Report bugs. | Suggested by BrownHairedGirl | #UCB_webform 2380/3834

← Previous revision		Revision as of 09:53, 23 August 2022
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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>		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 \| 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=~~Archived~~ ~~copy~~ \|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>		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 \| 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.		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.

InternetArchiveBot: Rescuing 1 sources and tagging 0 as dead.) #IABot (v2.0.8.8) (Ost316 - 10334

2022-07-10T05:06:36Z

Rescuing 1 sources and tagging 0 as dead.) #IABot (v2.0.8.8) (Ost316 - 10334

← Previous revision		Revision as of 05:06, 10 July 2022
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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>		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 \| 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>http://de.saswiki.org/wiki/SAS_Global_Forum_2007 ~~{{Dead~~ ~~link~~\|date=~~March~~ ~~2022~~}}</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>		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 \| 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=Archived copy \|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.		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.

BrownHairedGirl: {{Dead link}} tagged 1 bare URL ref to 1 dead website: http://de.saswiki.org

2022-03-19T05:50:12Z

{{Dead link}} tagged 1 bare URL ref to 1 dead website: http://de.saswiki.org

← Previous revision		Revision as of 05:50, 19 March 2022
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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>		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 \| 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>http://de.saswiki.org/wiki/SAS_Global_Forum_2007</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>		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 \| 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>http://de.saswiki.org/wiki/SAS_Global_Forum_2007 {{Dead link\|date=March 2022}}</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.		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.

BrownHairedGirl: remove square brackets from bare URL inline refs. The URL is much more helpful than a random number

2022-03-15T06:22:00Z

remove square brackets from bare URL inline refs. The URL is much more helpful than a random number

← Previous revision		Revision as of 06:22, 15 March 2022
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	==Related Technologies==		==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">[http://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>		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">http://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==		==External links==

BrownHairedGirl: tag with {{Bare URL PDF}}

2022-03-10T15:40:39Z

tag with {{Bare URL PDF}}

← Previous revision		Revision as of 15:40, 10 March 2022
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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>		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 \| IT World Canada News\|date=31 October 2006}}</ref> 2007<ref>http://www2.sas.com/proceedings/forum2007/371-2007.pdf</ref><ref>http://de.saswiki.org/wiki/SAS_Global_Forum_2007</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</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 \| 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>http://de.saswiki.org/wiki/SAS_Global_Forum_2007</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.		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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2021-10-17T09:16:07Z

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← Previous revision		Revision as of 09:16, 17 October 2021
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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>		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>http://www.itworldcanada.com/article/business-intelligence-taking-the-sting-out-of-forecasting/7193</ref> 2007<ref>http://www2.sas.com/proceedings/forum2007/371-2007.pdf</ref><ref>http://de.saswiki.org/wiki/SAS_Global_Forum_2007</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</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 \| IT World Canada News\|date=31 October 2006}}</ref> 2007<ref>http://www2.sas.com/proceedings/forum2007/371-2007.pdf</ref><ref>http://de.saswiki.org/wiki/SAS_Global_Forum_2007</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</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.		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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linking

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	===Translating models into SQL code===		===Translating models into SQL code===
	In this type of in-database processing, a predictive model is converted from its source language into SQL that can run in the database usually in a stored procedure. Many analytic model-building tools have the ability to export their models in either SQL or [[PMML]] (Predictive Modeling Markup Language). Once the SQL is loaded into a stored procedure, values can be passed in through parameters and the model is executed natively in the database. Tools that can use this approach include SAS, SPSS, R and KXEN.		In this type of in-database processing, a predictive model is converted from its source language into SQL that can run in the database usually in a [[stored procedure]]. Many analytic model-building tools have the ability to export their models in either SQL or [[PMML]] (Predictive Modeling Markup Language). Once the SQL is loaded into a stored procedure, values can be passed in through parameters and the model is executed natively in the database. Tools that can use this approach include SAS, SPSS, R and KXEN.

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