Revolution Analytics Podcast

547 views
426 views

Published on

In this presentation from Revolution Analytics, Bill Jacobs presents: Are You Ready for Big Data Analytics?

"Revolution Analytics delivers advanced analytics software at half the cost of existing solutions. By building on open source R—the world's most powerful statistics software—with innovations in big data analysis, integration and user experience, Revolution Analytics meets the demands and requirements of modern data-driven businesses."

Learn more: http://www.revolutionanalytics.com
Watch the presentation video: http://wp.me/p3RLEV-12S

Published in: Technology
0 Comments
0 Likes
Statistics
Notes
  • Be the first to comment

  • Be the first to like this

No Downloads
Views
Total views
547
On SlideShare
0
From Embeds
0
Number of Embeds
2
Actions
Shares
0
Downloads
15
Comments
0
Likes
0
Embeds 0
No embeds

No notes for slide
  • Remember that CRAN is a new term to IT professionals, and anyone who hasn’t learned much about R. Spend some time on it. CRAN = Community R Archive Network – a single repository of R algorithms, test data, evaluations. Use by nearly all R programmers.
  • Who is revolution
  • To understand how a typical customer might use RRE, it’s important to understand who a typical customer might be.users comprised of statisticians, data scientists, IT and academics across a wide variety of fields and industriesAlso point out flexibility of R solution, cross industries, CRAN offers incredible capabilities.Same with scalability, some customers use it to do desk top analysis and the exact same program is used in production servers elsewhere with no change to coding
  • Despite the growth, there are limitations with open source R, and these become more impactful as either the scale of the data grows or the number of users within an organizationRevo addresses these points to offer a more complete solutionCompare and contrast
  • This slide presents a way to distinguish ourselves from the open source versions of R, particularly those “supported” by platform vendors who bundle it. Explain that with this slide we are illustrating orders of magnitude performance improvement overall.Key advances are:Multi-threading and Multi-Core execution which allows parallel processors in a server to work together.Memory management that enables algorithms to use a combination of memory and disk, alleviating a long-standing problem with R, that of being limited by amount of physical memory.Parallelization in all its forms, but most importantly, the PEMA algorithms in ScaleR that work across clusters of servers – both in Hadoop and in cluster operating systems, to fully parallelize key statistics algorithms.
  • This slide presents a way to distinguish ourselves from the open source versions of R, particularly those “supported” by platform vendors who bundle it. Explain that with this slide we are illustrating orders of magnitude performance improvement overall.Key advances are:Multi-threading and Multi-Core execution which allows parallel processors in a server to work together.Memory management that enables algorithms to use a combination of memory and disk, alleviating a long-standing problem with R, that of being limited by amount of physical memory.Parallelization in all its forms, but most importantly, the PEMA algorithms in ScaleR that work across clusters of servers – both in Hadoop and in cluster operating systems, to fully parallelize key statistics algorithms.
  • DeployR Examples at: http://50.57.191.94/revolution/docs/examples/User:testuserPassword: secret
  • Revolution Analytics Podcast

    1. 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
    2. 2. Revolution Confidential 2
    3. 3. Revolution Confidential 3 Key Big Data Challenge: The Analytics Talent Pool
    4. 4. Revolution Confidential 4 The Analytics Talent Pool with R 2 Million R Users
    5. 5. Revolution Confidential 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. 5
    6. 6. Revolution Confidential 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 6
    7. 7. Revolution Confidential Revolution Analytics - Overview 7 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
    8. 8. Revolution Confidential Digital Media & Retail 200+ Customer Stories Finance & Insurance Healthcare & Life Sciences Manufacturing & High TechAcademic & Gov’t 8
    9. 9. Revolution Confidential Revolution R Enterprise 9 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
    10. 10. Revolution Confidential Aditional Technology Challenges Accompanying Big Data Analytics Efforts 10 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
    11. 11. Revolution Confidential 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
    12. 12. Revolution Confidential 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 12 First, we enhance and accelerate the Open Source R interpreter.
    13. 13. Revolution Confidential Open Source R performance: Multi-threaded Math Open Source R 13 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
    14. 14. Revolution Confidential 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 14 Second, we built a platform for hosting R with Big Data on a variety of massively parallel platforms.
    15. 15. Revolution Confidential Unparalleled Big Data Big Analytics Scale, Performance & Innovation 15 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
    16. 16. Revolution Confidential Analytic Personas and their Tools 16 Analytic Consumer Business Analyst Power Analyst Data Scientist Information Technologist Right Tool, Right Problem
    17. 17. Revolution Confidential 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 17
    18. 18. Revolution Confidential 18
    19. 19. Revolution Confidential 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? 19 ?
    20. 20. Revolution Confidential Portability and Investment Assurance: Write Once – Deploy Anywhere 20 Servers Server Clusters EDWs and Analytical DBMSs Hadoop (coming soon!) Write it Once. Deploy it Anywhere Workstations
    21. 21. Revolution Confidential 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 21
    22. 22. Revolution Confidential 22 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?
    23. 23. Revolution Confidential 23 Thank You.

    ×