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AWS Customer Presentation - How Runa uses AWS

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Robert Berger discusses how Runa uses AWS at the AWS Startup Tour - SV - 2010

Robert Berger discusses how Runa uses AWS at the AWS Startup Tour - SV - 2010

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Transcript

  • 1. Runa on AWS
    Big Data & Machine Intelligence for a SaaS Startup
  • 2. Runa
  • 3. aSaaS
  • 4. convertsShoppers to Buyers
  • 5. forOnline Commerce Sites
  • 6. by presentingDynamic Personalized Promotions
  • 7. on theMerchant’s Website
  • 8. inReal-Time
  • 9. in the Shopping Flow
  • 10. Tech Challenges
  • 11. Big Data
  • 12. JavaScript client collects activity on every Merchant page for every Shopper
  • 13. One or more Ajax call & Event Store to Runa per Merchant page view
  • 14. Step function increase of calls and stores as each new Merchant added
  • 15. We capture everything we can and store it forever
  • 16. Expecting to grow to thousands of merchants
  • 17. That’s a lot of Data
  • 18. Processing Data withMachine Intelligence
  • 19. Batch Processing forStatistical Analysisand Reports
  • 20. Real-Time Rule based inserts of Promotions
  • 21. Tech Challenges Synopsis
    Big Data & Processing
    Step Function Growth
    Batch Processing
    Real-Time Promotions
  • 22. Why AWS for Runa?
  • 23. At First(a couple years ago)
  • 24. Not Much Money in the Bank
  • 25. Didn’t Know exactly what were making
  • 26. Or exactly how we were going to do it
  • 27. Prototyped with Ruby / Rails / MySQL
  • 28. ThenPrototype became Production
  • 29. EC2 & AWS let us scale the prototype to Beta Production
  • 30. Flexibility to incrementally refine service & infrastructure
  • 31. Confidence we could scale as we added Merchants
  • 32. More RecentlyIncrementally added next-gen Tech & Full Production
  • 33. Goal: Everything Horizontally Scalable
  • 34. Batch Processing & Infinite StorageMap / Reduce& BigTable viaHadoop & HBase
  • 35. Flexible Real-Timeparallel processingvia Clojure / Swarmiji
  • 36.
  • 37.
  • 38. Deployment & Configuration ManagementviaOpscode Chef
  • 39. Good Things
  • 40. Able to Start Small
  • 41. ThenGROW BIGGER
  • 42. Having the flexibility to throw “Hardware” at our Prototype got us to market faster
  • 43. Ability to launch test and staging environments almost at will
  • 44. “Hardware” as “Software”
  • 45.
  • 46. Living in “interesting” times
  • 47. Managing Complexitylots of moving parts
  • 48. Easy to launch a few instances
  • 49. Impossible to manage horizontal stacks“by hand”
  • 50. Must have tool like Opscode Chef
  • 51. Chef automates deployment & puts it under Revision Control
  • 52. There’s going to be some blood when using cutting edge tech
  • 53. Lots of Learning Curves to climb
  • 54. Useful Monitoring is hard but Critical
  • 55. HBase on AWS may be dangerousbecause of Hadoop namenode SPOF
  • 56. EC2 bill can surprise you if you cavalierly deploy multiple versions of horizontally scalable environments
  • 57. Could not do our startup without AWS or lots more VC Funding

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