Hortonworks presentation from Cowen Big Data Day for financial industry analysts

1. Hortonworks Eric Baldeschwieler, Co-Founder and CEO September 2011 Overview for Cowen Big Data Day 2011 © Hortonworks Inc. 2011

2. Agenda Hortonworks Apache Hadoop Use cases Hadoop in the Enterprise Market Strategy 2 © Hortonworks Inc. 2011

3. About Hortonworks – Basics Founded – July 1st, 2011 22 architects & committers from Yahoo! Mission – Architect the future of Big Data Revolutionize and commoditize the storage and processing of Big Data via open source Vision – Half of the worlds data will be stored in Hadoop within five years 3 © Hortonworks Inc. 2011

4. About Hortonworks – Game Plan Support the growth of a huge Apache Hadoop ecosystem Invest in ease of use, management, and other enterprise features Define APIs for ISVs, OEMs and others to integrate with Apache Hadoop Continue to invest in advancing the Hadoop core, remain the experts Contribute all of our work to Apache Profit by providing training & support to the Hadoop community 4 © Hortonworks Inc. 2011

5. Credentials Technical: key architects and committers from Yahoo! Hadoop engineering team Delivered every major Apache Hadoop release since 0.1 Highest concentration of Apache Hadoop committers Driving innovation across entire Apache Hadoop stack Experience managing world’s largest deployment Access to Yahoo!’s 1,000+ users and 42k+ nodes for testing, QA, etc. Business operations: team of highly successful open source veterans Led by Rob Bearden, former COO of SpringSource & JBoss Investors: backed by Benchmark Capital and Yahoo! 5 © Hortonworks Inc. 2011

6. What is Apache Hadoop? Set of open source projects Owned by Apache Software Foundation Transforms commodity hardware into a service that: Stores petabytes of data reliably (HDFS) Allows huge distributed computations (MapReduce) Key attributes: Redundant and reliable Doesn’t stop or lose data even if hardware fails Easy to program Extremely powerful Allows the development of big data algorithms & tools Batch processing centric Runs on commodity hardware Computers & network 6 © Hortonworks Inc. 2011

7. Typical Hadoop Applications 7 data analytics advertising optimization machine learning search ranking Mail anti-spam advertising data systems audience, ad and search pipelines ad selection Website personalization Content Optimization ad inventory prediction user interest prediction © Hortonworks Inc. 2011

8. Who Builds Hadoop?Lines of code contributed since Hadoop inception 8 © Hortonworks Inc. 2011

9. Who Builds Hadoop?Lines of code contributed in 2011 9 © Hortonworks Inc. 2011

10. , early adopters Scale and productize Hadoop 2006 – present Other Internet Companies Add tools / frameworks, enhance Hadoop 2008 – present Service Providers Provide training, support, hosting 2010 – present Apache Hadoop A Brief History Nascent / 2011 Wide Enterprise Adoption Funds further development, enhancements 10 © Hortonworks Inc. 2011

11. HADOOP @ YAHOO! 40K+ Servers 170 PB Storage 5M+ Monthly Jobs 1000+ Active users © Yahoo 2011 11

12. CASE STUDY YAHOO! HOMEPAGE twice the engagement Personalized for each visitor Result: twice the engagement News Interests Top Searches Recommended links +43% clicks vs. editor selected +79% clicks vs. randomly selected +160% clicks vs. one size fits all © Yahoo 2011 12

14. Users - Interests

15. Five Minute Production

16. Weekly Categorization models»Machine learning to build ever better categorization models CATEGORIZATION MODELS (weekly) USER BEHAVIOR PRODUCTION HADOOP CLUSTER »Identify user interests using Categorization models SERVING MAPS (every 5 minutes) USER BEHAVIOR Build customized home pages with latest data (thousands / second) SERVING SYSTEMS ENGAGED USERS © Yahoo 2011 13 13

18. 5B+ deliveries/day

19. Antispam models retrained every few hours on Hadoop PRODUCTION “ 40% less spam than Hotmail and 55% less spam than Gmail “ © Yahoo 2011 14 14

20. Hadoop in the Enterprise © Hortonworks Inc. 2011 15

21. Big Data PlatformsCost per TB, Adoption Size of bubble = cost effectiveness of solution Source: 16 © Hortonworks Inc. 2011

22. Traditional Enterprise ArchitectureData Silos + ETL 17 Traditional Data Warehouses, BI & Analytics Serving Applications Web Serving NoSQLRDMS … Traditional ETL & Message buses EDW Data Marts BI / Analytics Traditional ETL & Message buses Serving Logs Social Media Sensor Data Text Systems … Unstructured Systems © Hortonworks Inc. 2011

23. Hadoop Enterprise ArchitectureConnecting All of Your Big Data 18 Traditional Data Warehouses, BI & Analytics Serving Applications Web Serving NoSQLRDMS … Traditional ETL & Message buses EDW Data Marts BI / Analytics Apache Hadoop EsTsL (s = Store) Custom Analytics Traditional ETL & Message buses Serving Logs Social Media Sensor Data Text Systems … Unstructured Systems © Hortonworks Inc. 2011

24. Hadoop Enterprise ArchitectureConnecting All of Your Big Data 19 Traditional Data Warehouses, BI & Analytics Serving Applications Web Serving NoSQLRDMS … Traditional ETL & Message buses EDW Data Marts BI / Analytics Apache Hadoop EsTsL (s = Store) Custom Analytics Gartner predicts 800% data growth over next 5 years 80-90% of data produced today is unstructured Traditional ETL & Message buses Serving Logs Social Media Sensor Data Text Systems … Unstructured Systems © Hortonworks Inc. 2011

25. The Hadoop Market © Hortonworks Inc. 2011 20

26. Market Drivers for Apache Hadoop Business drivers Identified high value projects that require use of more data Belief that there is great ROI in mastering big data Financial drivers Growing cost of data systems as proportion of IT spend Cost advantage of commodity hardware + open source Enables departmental-level big data strategies Technical drivers Existing solutions failing under growing requirements 3Vs - Volume, velocity, variety Proliferation of unstructured data 21 Significant opportunity for Hadoop in enterprise data architectures © Hortonworks Inc. 2011

27. Market Opportunity for Hadoop Current Apache Hadoop can become de facto platform for managing unstructured data in the enterprise Enable new breed of applications to be built on top of Apache Hadoop Future Hadoop becomes the next generation enterprise data architecture 22 © Hortonworks Inc. 2011

28. Market Dynamics Technology & knowledge gaps are preventing Apache Hadoop from becoming an enterprise standard Difficult to install and deploy Hadoop projects Lack of technical content to assist Demand for knowledgeable developers far exceeds supply Virtually every F500 company is constructing a Hadoop strategy But most are still in POC/experimentation phase with Hadoop Top ISV/OEMs working to create Hadoop strategies Driven by customer demand Community is becoming increasingly confused by all of the noise Multiple distributions, many vendor announcements Fear of market fragmentation 23 © Hortonworks Inc. 2011

29. Conclusion There is not a Hadoop market to “win” today Most organizations haven’t moved to full-scale production Lack of mass adoption limiting short-term monetization opportunities Need to drive Apache Hadoop as a unifying standard In order to succeed, we need to enable the market Continue investment to overcome technology gaps Enable a vibrant partner ecosystem Expand availability of content and services to address knowledge gaps How will Hortonworks do that? 24 © Hortonworks Inc. 2011

30. Hortonworks Strategy © Hortonworks Inc. 2011 25

32. Complete transparency

33. All code contributed back to ApacheAnyone should be able to easily deploy the Hadoop projects from Apache 26 © Hortonworks Inc. 2011

34. HortonworksStrategy #2Enable a Vibrant Ecosystem Unify the community around a strong Apache Hadoop offering Make Apache Hadoop easier to integrate & extend Work closely with partners to define and build open APIs Everything contributed back to Apache Provide enablement services as necessary to optimize integration 27 Integration & Services Partners Hadoop Application Partners DW, Analytics & BI Partners Serving & Unstructured Data Systems Partners Hardware Partners Cloud & Hosting Platform Partners © Hortonworks Inc. 2011

35. Hortonworks Strategy #3Overcome Knowledge Gaps Improve user experience with Apache Hadoop software Binaries, installers, etc. Expand Apache Hadoop technical content Core content on Apache.org Docs, installation guides, etc. Advanced tools on Hortonworks.com Best practices, screencasts, forums, etc. Extensive Hadoop training & certification program Expert technical support services 28 © Hortonworks Inc. 2011

36. Rationale for Hortonworks Strategy Strong interest from community (enterprises and ISV/OEMs) in a complete, enterprise-viable, Apache Hadoop platform Strong desire for core to remain unified and strong, avoid UNIX wars II Fremium model seen as a barrier to growth and adoption Highly defensible because of Hortonworks leadership in core projects Proven experience executing open source business models Rob Bearden & Benchmark 29 © Hortonworks Inc. 2011

37. 30 Thank You. © Hortonworks Inc. 2011