1. Revolution Analytics Overview of Revolution R Enterprise
For the Dallas R User’s Group
Joseph B. Rickert, Marketing Manager

2. Agenda Revolution Analytics Today Revolution R Enterprise
Revolution Analytics in the Enterprise
Big Data with RevoScaleR
Deploying R Throughout the Enterprise with RevoDeployR

3. Corporate Overview & Quick Facts
“Revolution Analytics is the leading commercial provider of software and support for the open-source R statistical computing language.”
Founded
2008 (as REvolution Computing)
Office Locations
Palo Alto (HQ), Seattle (Eng)
CEO
David Rich
Number of Employees
40+
Number of customers
100+
Investors
Northbridge Venture Partners, Intel Capital, Presidio Ventures
Our Mission: “To make Revolution the world-leading predictive analytics company while continuously improving and contributing to the R experience for the open source community.”

4. Open Source Analytics for the Enterprise
Most advanced statistical analysis software available
The professor who invented analytic software for the experts now wants to take it to the masses
Half the cost of commercial alternatives
Power
Productivity
Enterprise Readiness
2M+ Users
2,500+ Applications
Finance
Statistics
Life Sciences
Predictive Analytics
Manufacturing
Retail
Data Mining
Telecom
Social Media
Visualization
Government

5. Revolution R Enterprise
Productivity

6. Revolution R Enterprise has Open-Source R Engine at the core
2,500 community packages and growing exponentially
Multi-Threaded Math Libraries
Web Services API
Big Data Analysis
Parallel Tools
Technical Support
Developer IDE
R Engine Language Libraries
Community Packages
Build Assurance

7. A network of partners for integrated, large-scale data analysis
Advanced Analytics
Deployment / Consumption
Data Infrastructure

8. Revolution R Enterprise
Performance

9. Performance: Intel MKL Math Libraries
Open Source R
Revolution R Enterprise
Computation (4-core laptop)
Open Source R
2.13.2
Revolution R Enterprise
5.0
Speedup
(4-core laptop)
Linear Algebra1
Matrix Multiply
174.6 sec
10.4 sec
15.8x
Cholesky Factorization
25.7 sec
1.4 sec
17.6x
Linear Discriminant Analysis
224.4 sec
20.1 sec
7.6x
General R Benchmarks2
R Benchmarks (Matrix Functions)
24.9 sec
3.8 sec
5.5x
R Benchmarks (Program Control)
4.7 sec
4.6 sec
Not appreciable 1. 2.

10. Revolution R Enterprise
Big Data Analysis

11. A common analytic platform across big data architectures
Hadoop
File Based
In-database
Lots of funding of storage companies
Large enterprises will not choose only one of these, they will likely use several; each has pros and cons
We are the only company that can span each of these technologically and relationship wise
The ability to not be forced to silo analysis and/or re-write algorithms offers tremendous value to companies

12. Two Big Data problems: capacity and speed
Capacity: problems handling the size of data sets or models
Data too big to fit into memory
Even if it can fit, there are limits on what can be done
Even simple data management can be extremely challenging
Speed: even without a capacity limit, computation may be too slow to be useful
Capacity: problems handling the size of data sets or models
Data too big to fit into memory
Even if it can fit, there are limits on what can be done
Even simple data management can be extremely challenging
Speed: even without a capacity limit, computation may be too slow to be useful

13. RevoScaleR: Big Data Analysis for Revolution R Enterprise
Distributed Statistical Algorithms
External Memory Programming Framework
Addresses performance by distributing computations between cores and computers
Addresses capacity through a collection of functions for chunking through massive data files
R Language
Interface
XDF File Format
A novel high-speed file format designed specifically to support statistical analyses
Familiar, high-prodictivity programming paradigm for R users

14. The basis for a solution for capacity, speed, distributed and streaming data – PEMA’s
Parallel external memory algorithms (PEMA’s) allow solution of both capacity and speed problems, and can deal with distributed and streaming data
External memory algorithms are those that allow computations to be split into pieces so that not all data has to be in memory at one time
It is possible to “automatically” parallelize and distribute such algorithms

15. Multicore Processor (4, 8, 16+ cores)
RevoScaleR on a Multicore Server
Shared Memory
Data
Data
Data
Disk
Core 0
(Thread 0)
Core 1
(Thread 1)
Core 2
(Thread 2)
Core n
(Thread n)
Multicore Processor (4, 8, 16+ cores)
RevoScaleR
A RevoScaleR algorithm is provided a data source as input
The algorithm loops over data, reading a block at a time. Blocks of data are read by a separate worker thread (Thread 0).
Other worker threads (Threads 1..n) process the data block from the previous iteration of the data loop and update intermediate results objects in memory
When all of the data is processed a master results object is created from the intermediate results objects

16. RevoScaleR for Distributed Computing Clusters
Compute Node
(RevoScaleR)
Data
Partition
Portions of the data source are made available to each compute node
RevoScaleR on the master node assigns a task to each compute node
Each compute node independently processes its data, and returns it’s intermediate results back to the master node
master node aggregates all of the intermediate results from each compute node and produces the final result
Compute Node
(RevoScaleR)
Data
Partition
Master Node
(RevoScaleR)
Compute Node
(RevoScaleR)
Data
Partition
Compute Node
(RevoScaleR)
Data
Partition

17. Platform-agnostic Big Data Analytics
Set “compute context” to define hardware (one line of code)
Native job-scheduler handles distribution, monitoring, failover etc.
Same code runs on other supported architectures
Just change compute context
Supported architectures:
Windows: Microsoft HPC Server
Linux: Platform Computing LSF (coming 2012)
42 seconds instead of 6 minutes

18. R and Hadoop
Hadoop offers a scalable infrastructure for processing massive amounts of data
Storage – HDFS, HBASE
Distributed Computing - MapReduce
R is a statistical programming language for developing advanced analytic applications
Currently, writing analytics for Hadoop requires a combination of Java, pig, Python, …
The Rhadoop project makes it possible to write PEMAs for Hadoop using the R language alone.

19. Massively parallel/distributed analytics: RevoConnectR for Hadoop
HDFS
HBASE
Write Map-Reduce analytics using only R code with these R packages:
rhdfs - R and HDFS
rhbase - R and HBASE
rmr - R and MapReduce R
Thrift
Map or Reduce
rhbase
Task Node
rhdfs
Revolution R Client
More information at:
bit.ly/r-hadoop
Job Tracker
rmr

20. In-Database Execution with IBM Netezza

21. Revolution R Enterprise
Enterprise Deployment

22. Revolution R Web Services: RevoDeployR
Data Sources & Creation of Analytics
Consumption of Analytics & Results
Data Analysis
Revolution “RevoDeployR”
R / Statistical Modeling Expert
Deployment Expert
Business Intelligence
Interactive Web Apps
Enables Models and Analytics to be consumed by end users through BI applications
Risk Analysis
Sales Forecasting
Implemented as a collection of Web Services that allow easy integration into many different 3rd party applications
Same technology that provides the statistical engine for the GUI
Cloud / SaaS

23. Twitter: @RevolutionR
Thank you.
The leading commercial provider of software and support for the popular open source R statistics language.