Presented to the Rich Report, August 30 2013. Listen to the audio podcast at: http://inside-bigdata.com/2013/09/04/slidecast-ready-big-data-analytics/

1. Revolution Confidential
Are You Ready for Big
Data Big Analytics?
September, 2013
Bill Jacobs
Director, Product Marketing
Revolution Analytics
@bill_jacobs
Revolution Analytics
@RevolutionR

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Key Big Data Challenge: The Analytics
Talent Pool

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The Analytics Talent Pool with R
2 Million R Users

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What Language is Most Popular for Data
Mining and Data Science?
Survey Question:
“What programming/statistics languages you used for an analytics /
data mining / data science work in 2013?”
Results:
R – 61%
Python – 39%
SQL - 37%
How does this compare to 2012?
“Highest growth was for Pig/Hive/Hadoop-based languages, R, and
SQL, while Perl, C/C++, and Unix tools declined…”
From 2013 KDNuggets Survey of 700 voters.
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The R Language: What Is It?
 A Language Platform…
 A Procedural Language optimized for Statistics and Data Science
 A Data Visualization Framework
 Provided as Open Source
 A Community…
 2M Statistical Analysis and Machine Learning Users
 Taught in Most University Statistics Programs
 Active User Groups Across the World
 An Ecosystem
 CRAN: 4500+ Freely Available Algorithms, Test Data and
Evaluations
 Many Applicable to Big Data If Scaled
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Revolution Analytics - Overview
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We are the only provider of a commercial analytics platform based on
the open source R statistical computing language.
Power
Productivity
Enterprise
Readiness
Stable,scalable
multi-platform
world-wide support
Easier to build and deploy analytic
applications
Professional services enablement
Distributed, high performance
analytics algorithms
World Wide Support Teams
• Standard and Premium Programs
• Technical Account Managers
• Customer Success Managers
Professional Services
• Architecture planning
• Systems Integration
• Advanced analytic applications
• Full life cycle projects

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Digital Media & Retail
200+ Customer Stories
Finance & Insurance Healthcare & Life Sciences
Manufacturing & High TechAcademic & Gov’t
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Revolution R Enterprise
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Revolution R Enterprise
is the only commercial big data analytics platform
that provides Big Data Big Analytics based on R.
Portable Across Enterprise Platforms
High Performance, Scalable Analytics
Easier to Build & Deploy

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Additional Technology Challenges
Accompanying Big Data Analytics Efforts
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Big Data
• New Data
Sources
• Data Variety &
Velocity
• Fine Grain
Control
• Data Movement,
Memory Limits
Complex
Computation
• Experimentation
• Many Small
Models
• Ensemble
Models
• Simulation
Enterprise
Readiness
• Heterogeneous
Landscape
• Write Once,
Deploy Anywhere
• Skill Shortage
• Production
Support
Production
Efficiency
• Shorter Model
Shelf Life
• Volume of
Models
• Long End-to-End
Cycle Time
• Pace of Decision
Accelerated

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Open Source R Drives Analytical Innovation
… with some limitations for enterprises
but has some limitations for Enterprise Deployment
Memory Bound
Large Data & Cluster-Based
Storage Management
Single Threaded
Scalable, multi-threaded,
parallel processing
Community Support
Commercial production
support and professional
services teams
Innovative – 5000
packages+,
exponential growth
Ability to combine
with open source R
packages where
needed
Operate on
bigger data
sizes
Increased
speed of
analysis
Holistic
production
support
A key combination
of innovation and
scale
Results
limitations

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Big Data Speed @ Scale with
Revolution R Enterprise (RRE)
Fast Math Libraries
Parallelized Algorithms
In-Database Execution
Multi-Threaded Execution
Multi-Core Processing
In-Hadoop Execution
Memory Management
Parallelized User Code
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First, we enhance and
accelerate the Open
Source R interpreter.

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Open Source R performance:
Multi-threaded Math
Open
Source R
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Revolution R
Enterprise
Computation (4-core laptop) Open Source R Revolution R Speedup
Linear Algebra1
Matrix Multiply 176 sec 9.3 sec 18x
Cholesky Factorization 25.5 sec 1.3 sec 19x
Linear Discriminant Analysis 189 sec 74 sec 3x
General R Benchmarks2
R Benchmarks (Matrix Functions) 22 sec 3.5 sec 5x
R Benchmarks (Program Control) 5.6 sec 5.4 sec Not appreciable
1. http://www.revolutionanalytics.com/why-revolution-r/benchmarks.php
2. http://r.research.att.com/benchmarks/
Customers report 5-50x
performance improvements
compared to Open Source R —
without changing any code

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Big Data Speed @ Scale with
Revolution R Enterprise (RRE)
Fast Math Libraries
Parallelized Algorithms
In-Database Execution
Multi-Threaded Execution
Multi-Core Processing
In-Hadoop Execution
Memory Management
Parallelized User Code
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Second, we built a
platform for hosting R
with Big Data on a
variety of massively
parallel platforms.

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Unparalleled Big Data Big Analytics
Scale, Performance & Innovation
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1 + 1 = 1000’s
Performance
V
a
l
u
e
Revolution R Enterprise
+ =
Performance
Enhanced R
R Language
Open Source
R Analytic
Packages
Big Data
Distributed &
Parallel
Processing
&
Analytic Package
Big Data
Distributed &
Parallel
Processing
&
Analytic Package
Open Source
R Analytic
Packages
Performance Enhanced R

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Analytic Personas and their Tools
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Analytic
Consumer
Business
Analyst
Power
Analyst
Data
Scientist
Information
Technologist
Right Tool, Right Problem

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On-demand sales
forecasting
Real-time social
media sentiment
analysis
Create Custom, On-Demand Analytical Apps
Some Examples:
Leveraging the
power of R from
Microsoft tools
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Predicting Predictive Analytics
 What Are Your Use Cases?
 How Will Your Use Cases Evolve?
 What Platform Will Best Support Each?
 Who’s Platform Excel Tomorrow?
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?

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Portability and Investment Assurance:
Write Once – Deploy Anywhere
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Servers
Server Clusters
EDWs and Analytical DBMSs
Hadoop (coming soon!)
Write it Once.
Deploy it Anywhere
Workstations

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Summary.
 R is Hot.
 Revolution R Enterprise:
 Scales R to Big Data.
 Scales Performance on Big Data Platforms
 Is Commercially Supported
 Is Broadly Deployable
 Allows you to WODA!
 Revolution Analytics Maximizes Results, While
Minimizing Near-Term and Long-Term Risks
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www.revolutionanalytics.com 650.646.9545 Twitter: @RevolutionR
The leading commercial provider of software and support for the popular
open source R statistics language.
Next steps?

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Thank You.