Thursday Feb 16, 2012

R and Database Access

In an enterprise, databases are typically where data reside. So where data analytics are required, it's important for R and the database to work well together. The more seamlessly and naturally R users can access data, the easier it is to produce results. R users may leverage ODBC, JDBC, or similar types of connectivity to access database-resident data. However, this  requires working with SQL to formulate queries to process or filter data in the database, or to pull data into the R environment for further processing using R. If R users, statisticians, or data analysts are unfamiliar with SQL or database tasks, or don't have database access, they often consult IT for data extracts.

Not having direct access to database-resident data introduces delays in obtaining data, and can make near real-time analytics impossible. In some instances, users request data sets much larger than required to avoid multiple requests to IT. Of course, this approach introduces costs of exporting, moving, and storing data, along with the associated backup, recovery, and security risks.

Oracle R Enterprise eliminates the need to know SQL to work with database-resident data. Through the Oracle R Enterprise transparency layer, R users can access data stored in tables and views as virtual data frames. Base R functions performed on these "ore.frames" are overloaded to generate SQL which is transparently sent to Oracle Database for execution - leveraging the database as a high-performance computational engine.

Check out Oracle R Enterprise for examples of the interface, documentation, and a link to download Oracle R Enterprise.

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