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Building Metrics In-House with Keen IO

E37807353c2df74f78a25a267f17dccc?s=47 Keen
September 30, 2015

Building Metrics In-House with Keen IO

Kate Heddleston shares how she built analytics in-house with Keen IO's APIs and analytics infrastructure.

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Keen

September 30, 2015
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  1. Building Metrics In- House Using Keen IO

  2. Kate Heddleston kateheddleston.com @heddle317

  3. Building Metrics In-House Why use Keen IO? Keen IO’s API

  4. What does it look like to build metrics in-house?

  5. Infrastructure

  6. Skylight Coordinator Kafka Storm Spout Bolt Cassandra Java

  7. -Tom Dale and Yehuda Katz “One year of research. Six

    months to rewrite.”
  8. Skylight Coordinator Kafka Storm Spout Bolt Cassandra Java

  9. Keen Testing Monitoring/ validation Data fail- overs and uptime Separate

    infrastructure
  10. –New Relic Website Marketing Person “New Relic Insights is powered

    by a highly distributed cloud-hosted event database with an innovative architecture that does not require indexing. The database runs on a super-cluster built to support our thousands of customers — giving you access to a database that can query your big data in seconds.” New Relic
  11. New Relic Challenges Database tuning Optimizing for reads vs. writes

    Determining which metrics can be aggregated.
  12. Building metrics infrastructure is really hard…

  13. UX

  14. Metrics can easily end up like…

  15. …or…

  16. …or…

  17. Metrics don’t mean anything unless they are answering specific questions.

  18. Data Visualization Telling meaningful stories with your data.

  19. Building Metrics UX is really hard…

  20. …how many hard problems do you want to solve at

    once?
  21. Why use Keen IO?

  22. 1. Fast prototyping. 2. They deal with infrastructure while you

    deal with creating customer value. 3. You don’t have to hire as many software engineers.
  23. What does Keen’s API look like?

  24. Event Collection Timeframe and interval Group By Filter Analyses

  25. None
  26. Event Collection Timeframe and interval Group By Filter Analyses

  27. None
  28. Event Collection Timeframe and interval Group By Filter Analyses

  29. None
  30. Event Collection Timeframe and interval Group By Filter Analyses

  31. None
  32. Event Collection Timeframe and interval Group By Filter Analyses

  33. None
  34. Event Collection Timeframe and interval Group By Filter Analyses

  35. None
  36. Conlusion

  37. Solve less hard problems.

  38. Use Keen IO

  39. Questions Kate Heddleston kateheddleston.com @heddle317

  40. Resources ✤ Skylight - Tom Dale and Yehuda Katz ✤

    http://www.confreaks.com/videos/3394-railsconf-how-to-build-a-smart-profiler-for-rails ✤ Keen Infrastructure Stack ✤ http://blog.leanstack.io/keen-io-tech-stack/ ✤ Data Visualization Experts ✤ http://www.information-management.com/resource-center/?id=10024000 ✤ New Relic Infrastructure ✤ http://newrelic.com/insights/technology#the-database ✤ http://highscalability.com/blog/2011/7/18/new-relic-architecture-collecting-20-billion-metrics- a-day.html (2011) ✤ Keen API Docs ✤ https://keen.io/docs/data-analysis/