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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

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AWS Customer Presentation - How Runa uses AWS

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

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