What is the difference between the 3 architectural components of Microsoft R Server? Lately, I've been experimenting with Microsoft R Server(MRS) and SQL Server R Services. I've come across that MRS is built on architectural components like ScaleR algorithms optimized for fast parallel execution, DistributedR parallel computing framework for managing compute resources, and ConnectR for versatile connections to data sources.


I've also learnt (and got confused) that 


ScaleR algorithms are basically functions of the RevoScaleR package built on the DistributedR framework.
ConnectR component of the architecture is also a bunch of RevoScaleR functions / adapters to access a variety of data sources.


My question is:
What is RevoScaleR in relation to DistributedR and ConnectR?

Where does DeployR fit into all this?


In general, I want to know the high level differences between
RevoScaleR package
DistributedR
ScaleR
ConnectR
DeployR
RevoScaleR package &amp; ScaleR are same (I think after MS acquisition its became scaleR) 
A new file format especially designed for large files such as : 
XDF File Format (block based data format)
RxDataSource (designed to support the use of external memory algorithms with .xdf files.)
PEMA  based Algorithms
External memory implementations of the statistical algorithms most commonly used with large data sets such as : rxSummary, rxCrossTabs, rxLinMod. rxKMeans, rxGlm etc.
An extensible programming framework that allows R programmers to write their own external memory algorithms that can take advantage of ScaleR Enterprise’s new Big Data capabilities.

DistributedR for supporting big data framework
ConnectR It has all connectors to interact with other data sources
DeployR is used to integrate R with any application programming