In-database analytics using R

In-database functions can use performance-enhancing features of the underlying database management system, such as columnar technology. Using in-database functions also avoids security issues that are associated with extracting data and ensures that the data that is being analyzed is as current as possible. Some in-database functions additionally use lazy loading to load only those parts of the data that are actually required, to further increase efficiency.

Both in-application and in-database functions are equally easy to use. An in-application function operates on an R construct called a data frame, which is a container that holds, in memory, a copy of the data to be processed. An in-database function operates on a similar construct called an IDA data frame. An IDA data frame does not hold any data directly. Instead, it holds a reference to a table or view in the database or to a selection of rows and columns within that table or view. When a function or method is applied to an IDA data frame, it is usually not run in the application, but is translated into an SQL query. The query is then run against the database, and the result is translated into an R object.