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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
===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
==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">
==External links==
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