Runa leverages AWS for big data and machine learning
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Robert Berger discusses how Runa uses AWS at the AWS Startup Tour - SV - 2010
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Runa leverages AWS for big data and machine learning
- 1. Runa on AWS Big Data & Machine Intelligence for a SaaS Startup
- 2. Runa
- 3. aSaaS
- 6. by presentingDynamic Personalized Promotions
- 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
- 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
- 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
- 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