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Revolution Analytics: a 5-minute history
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Revolution Analytics: a 5-minute history


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Revolution Analytics was the first company dedicated to the R Project. This presentation from useR! 2014 covers the history of Revolution Analytics since its founding in 2007 and its contributions to …

Revolution Analytics was the first company dedicated to the R Project. This presentation from useR! 2014 covers the history of Revolution Analytics since its founding in 2007 and its contributions to the R project and community.

Published in: Technology

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    • 1. A 5-minute history David M Smith Chief Community Officer @revodavid Sponsor Presentation, useR! 2014
    • 2. 2 2007: The Beginning
    • 3. 3 2008: Revolutions Blog
    • 4. R in the News 2009 New York Times: Data Analysts Captivated by R’s Power 4
    • 5. 5 2009 Revolution R Enterprise version 3 First R Debugging IDE
    • 6. 6 2010: User Group Sponsorships 141 R User Groups
    • 7. Rows of data 1 billion 1 billion Parameters “just a few” 7 Time 80 seconds 44 seconds Data location In memory On disk Nodes 32 5 Cores 384 20 RAM 1,536 GB 80 GB Double 45% 1/6th 5% 5% Revolution R is faster on the same amount of data, despite using approximately a 20th as many cores, a 20th as much RAM, a 6th as many nodes, and not pre-loading data into RAM. Bottom Line: Revolution R Enterprise Performance = Greatly Reduced TCO *As published by SAS in HPC Wire, April 21, 2011 Logistic Regression: 7 2010: Head to Head with SAS
    • 8. 8 2011: RHadoop
    • 9. 2012: Clusters, Hadoop and Databases Write Once  Deploy Anywhere rxSetComputeContext("local") # DEFAULT rxSetComputeContext(RxHadoopMR(<data, server environment arguments>)) # Summarize and calculate descriptive statistics from the data airDS data set adsSummary <- rxSummary(~ArrDelay+CRSDepTime+DayOfWeek, data = airDS) # Fit Linear Model arrDelayLm1 <- rxLinMod(ArrDelay ~ DayOfWeek, data = airDS); summary(arrDelayLm1) rxSetComputeContext(RxHpcServer(<data, server environment arguments>)) rxSetComputeContext(RxLsfCluster(<data, server environment arguments>)) Same code to be run anywhere ….. Local System (default)     Set the desired compute context for code execution….. rxSetComputeContext(RxTeradata(<data, server environment arguments>)) 
    • 10. 10 2013 Shaking up the industry A Gartner Magic Quadrant Visionary
    • 11. 11 2014: Technical Support for Open Source R AdviseR™ from Revolution Analytics Technical support for open source R, from the R experts.  10x5 email and phone support  Support for R, validated packages, and third-party software connections  On-line case management and knowledgebase  Access to technical resources, documentation and user forums  Exclusive on-line webinars from community experts  Guaranteed response times Also available: expert hands-on and on-line training for R, from Revolution Analytics AcademyR. R SUPPORT 12 MONTHS $795 PER USER
    • 12. … and beyond! Continued growth and demand for R  R is the highest paid IT skill –, Jan 2014  R most-used data science language after SQL – O’Reilly, Jan 2014  R is used by 70% of data miners – Rexer, Sep 2013  R is #15 of all programming languages – RedMonk, Jan 2014  R growing faster than any other data science language – KDnuggets, Aug 2013  More than 2 million users worldwide R Usage Growth Rexer Data Miner Survey, 2007-2013 70% of data miners report using R R is the first choice of more data miners than any other software Source:
    • 13. Thank you Revolution Analytics is the leading commercial provider of software and support for the popular open source R statistics language., 1.855.GET.REVO, Twitter: @RevolutionR 13