Post on 26-Jan-2015
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Revolution Confidential
Are You Ready for Big Data Big Analytics? September, 2013
Bill JacobsDirector, Product MarketingRevolution 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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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 StoriesFinance & Insurance Healthcare & Life Sciences
Manufacturing & High TechAcademic & Gov’t
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Revolution R Enterprise
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
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 enterprisesbut has some limitations for Enterprise Deployment
Memory BoundLarge Data & Cluster-Based
Storage Management
Single ThreadedScalable, multi-threaded,
parallel processing
Community SupportCommercial 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
Revolution ConfidentialBig 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 MathOpen
Source R
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.php2. http://r.research.att.com/benchmarks/
Customers report 5-50x performance improvements
compared to Open Source R — without changing any code
Revolution ConfidentialBig 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 AnalyticsScale, Performance & Innovation
1 + 1 = 1000’s
Performance
Value
Revolution R Enterprise
+ =
Performance Enhanced R
R Language
Open Source R Analytic Packages
Big DataDistributed &
Parallel Processing
& Analytic Package
Big DataDistributed &
Parallel Processing
& Analytic Package
Open Source R Analytic Packages
Performance Enhanced R
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Analytic Personas and their Tools
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 AppsSome 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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Portability and Investment Assurance:Write Once – Deploy Anywhere
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.
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