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Microsoft and Revolution Analytics -- what's the add-value? 20150629

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Microsoft has been a leader in the enterprise analytics space for years. In 2014, Microsoft had already created R language functionality within Azure Machine Learning. On April 6, 2015, Microsoft and closed on a deal to acquire Revolution Analytics, a company focusing on scalable processing solutions initiated by the well-known R language. Many data science projects and initial demos do not need high-volume solutions: however, having a high-volume answer for the R language allows for planning or working toward the largest data science solutions.

This presentation describes the add-value for the Revolution Analytics acquisition. The talk covers 1) an overview of current data science technologies from Microsoft; 2) a description of the R language; 3) a brief review of the add-value for R with Azure Machine Learning, and 4) a description of the performance architecture and demo of the language constructs developed by Revolution Analytics. Most of the presentation will be focused on sections two and four. It is anticipated that these technologies will be partially if not fully integrated into SQL Server 2016.

Published in: Data & Analytics

Microsoft and Revolution Analytics -- what's the add-value? 20150629

  1. 1. Microsoft and Revolution Analytics: What’s the Add-Value? MARK TABLADILLO PH.D. – MICROSOFT MVP JUNE 29, 2015
  2. 2. Mark Tab  Consulting  Training  Teaching  Presenting  SQL Server MVP  Linked In  @MarkTabNet
  3. 3. Outline  1) an overview of current data science technologies from Microsoft;  2) a description of the R language;  3) a brief review of the add-value for R with Azure Machine Learning, and  4) a description of the performance architecture and demo of the language constructs developed by Revolution Analytics
  4. 4. Current Data Science Technologies • SQL Server License (Win OS) • Business Intelligence or Enterprise SQL Server Analysis Services Data Mining • Excel 2007 or Higher • X64 better Excel Data Mining Add-In • Free or Paid Tiers • Any OS Microsoft Azure Machine Learning • Open Source • Mono-Project, Visual Studio F# • SQL Server 2016Revolution Analytics
  5. 5. Data Scientist Interact directly with data Built-in to SQL Server Data Developer/DBA Manage data and analytics together Built-in advanced analytics In-database analytics Example Solutions • Fraud detection • Salesforecasting • Warehouse efficiency • Predictive maintenance Relational Data Analytic Library T-SQL Interface Extensibility ? R RIntegration 010010 100100 010101 Microsoft Azure Machine Learning Marketplace New R scripts 010010 100100 010101 010010 100100 010101 010010 100100 010101 010010 100100 010101 010010 100100 010101
  6. 6. AML Gallery ML Studio SSMS / R SSRS / CR Excel / PV Power BI.com Fisher’s Iris flower dataset machine learning
  7. 7. Description of the R Language R RSTUDIO RATTLE
  8. 8. Growth and Demand for R  R is the highest paid IT skill  Dice.com, 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: www.rexeranalytics.com
  9. 9. R with Azure Machine Learning
  10. 10. Revolution Analytics
  11. 11. 2007: The Beginning 13
  12. 12. 2008: Revolutions Blog 14
  13. 13. R in the News 15 2009 New York Times: Data Analysts Captivated by R’s Power
  14. 14. Revolution R Enterprise version 3 First R Debugging IDE 16
  15. 15. 2010: User Group Sponsorships 17 141 R User Groups
  16. 16. 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: 18 2010: Head to Head with SAS
  17. 17. 2011: RHadoop 19 github.com/RevolutionAnalytics/RHadoop
  18. 18. 2013 Shaking up the industry A Gartner Magic Quadrant Visionary 20
  19. 19. 2014: Technical Support for Open Source R AdviseR™ from Revolution Analytics 21 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. http://www.revolutionanalytics.com/adviser http://revolutionanalytics.com/academyr-training- education
  20. 20. Summary WATCH FOR SQL SERVER 2016
  21. 21. Abstract  Microsoft has been a leader in the enterprise analytics space for years. In 2014, Microsoft had already created R language functionality within Azure Machine Learning. On April 6, 2015, Microsoft and closed on a deal to acquire Revolution Analytics, a company focusing on scalable processing solutions initiated by the well-known R language. Many data science projects and initial demos do not need high-volume solutions: however, having a high-volume answer for the R language allows for planning or working toward the largest data science solutions.  This presentation describes the add-value for the Revolution Analytics acquisition. The talk covers 1) an overview of current data science technologies from Microsoft; 2) a description of the R language; 3) a brief review of the add-value for R with Azure Machine Learning, and 4) a description of the performance architecture and demo of the language constructs developed by Revolution Analytics. Most of the presentation will be focused on sections two and four. It is anticipated that these technologies will be partially if not fully integrated into SQL Server 2016.

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