Monday Sep 17, 2012

Podcast interview with Michael Kane

In this podcast interview with Michael Kane, Data Scientist and Associate Researcher at Yale University, Michael discusses the R statistical programming language, computational challenges associated with big data, and two projects involving data analysis he conducted on the stock market "flash crash" of May 6, 2010, and the tracking of transportation routes bird flu H5N1. Michael also worked with Oracle on Oracle R Enterprise, a component of the Advanced Analytics option to Oracle Database Enterprise Edition. In the closing segment of the interview, Michael comments on the relationship between the data analyst and the database administrator and how Oracle R Enterprise provides secure data management, transparent access to data, and improved performance to facilitate this relationship.

Listen now...

Monday Feb 20, 2012

Announcing Oracle R Distribution

Oracle has released the Oracle R Distribution, an Oracle-supported distribution of open source R. This is provided as a free download from Oracle. Support for Oracle R Distribution is provided to customers of the Oracle Advanced Analytics option and Oracle Big Data Appliance. The Oracle R Distribution facilitates enterprise acceptance of R, since the lack of a major corporate sponsor has made some companies concerned about fully adopting R. With the Oracle R Distribution, Oracle plans to contribute bug fixes and relevant enhancements to open source R.

Oracle has already taken responsibility for and contributed modifications to ROracle - an Oracle database interface (DBI) driver for R based on OCI. As ROracle is LGPL and used for Oracle Database connectivity from R, we are committed to ensuring this is the best package for Oracle connectivity.

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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The place for best practices, tips, and tricks for applying Oracle R Enterprise, Oracle R Distribution, ROracle, and Oracle R Advanced Analytics for Hadoop in both traditional and Big Data environments.

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