Microsoft R Server extends the base R language and Microsoft R Open with big-data capabilities. Specifically, it adds the RevoScaleR package, which creates an out-of-memory “CDF” data structure (so you can process data larger than available RAM), and algorithms that allow you to perform computations on such data using parallel and distributed algorithms. (A limited version of the RevoScaleR package is also included in the free Microsoft R Client.)
If you'd like to get up to speed on the capabilities of Microsoft R Server, my colleague Matt Parker has created a 4-part video series that introduces Microsoft R Server and delves into data ingestion, data management, and predictive modeling. The videos are embedded below, and you can also download the videos for offline viewing from Channel 9.
Part 2: Data Ingestion
This video covers the eXternal Data Frame (XDF) format, and functions for importing data from files and databases.
Part 3: Data Management
This video covers key functions for the standard data management tasks: creating and modifying variables, sorting, subsetting, deduplication, and merging datasets.
Part 4: Predictive Modeling
This video focuses on functions for exploring and summarizing data, building predictive models, and making predictions.
If you'd like to follow along with the videos on your own PC, you can download Microsoft R Client for free and use all of the functions described in the video.
Channel 9: Microsoft R Server 4-part series