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DATA @ NFLX (Tableau Conference 2014 Presentation)

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DATA @ NFLX (Tableau Conference 2014 Presentation)

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I presented this at a 2014 Tableau Conference session with Albert Wong.

Netflix relies on data to make decisions ranging from buying and recommending content, to improving the streaming experience on devices.

This presentation shares our Big Data analytics architecture and the tools used to make data accessible throughout our business, focusing on how Tableau fits into our organization and why it aligns well with our culture.

I presented this at a 2014 Tableau Conference session with Albert Wong.

Netflix relies on data to make decisions ranging from buying and recommending content, to improving the streaming experience on devices.

This presentation shares our Big Data analytics architecture and the tools used to make data accessible throughout our business, focusing on how Tableau fits into our organization and why it aligns well with our culture.

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DATA @ NFLX (Tableau Conference 2014 Presentation)

  1. 1. DATA @ NFLX Building a Culture of Analytics Everywhere Tableau Customer Conference 2014.09.09 Blake Irvine Manager, Device Analytics Data Science & Engineering birvine@netflix.com Albert Wong Manager, Reporting Platforms Cloud & Platform Engineering albwong@netflix.com
  2. 2. Netflix and data in the news... “Giving Viewers What They Want” --New York Times “The Science Behind the Netflix Algorithms That Decide What You’ll Watch Next” --Wired Data-Mining Boosts Netflix's Subscriber Base, Showbiz Clout --AdAge
  3. 3. BIG DATA
  4. 4. Big Data at Netflix Size ● 50+ million members ● 1000’s of devices ● 100’s of systems ● >300B data pipeline events daily ● >10B row tables daily Ubiquitous ● Data is everywhere ● Many complex systems ● Many engineering teams producing and consuming ● Non-streaming teams produce and consume data ● Culturally data driven
  5. 5. How do we innovate with Big Data?
  6. 6. TOOLS CULTURE
  7. 7. Tools DATA STORAGE DATA PROCESSORS DATABASE REPORTING Sting
  8. 8. Team Structure Data Science and Engineering Marketing data engineering reporting analyst Finance Product Engineering ... Business Functions
  9. 9. Team Structure Data Science and Engineering WE DO NOT WHAT Marketing data engineering reporting analyst Finance Product Engineering ...
  10. 10. Netflix Team Structure Data Science and Engineering data engineering reporting analyst Marketing data engineering reporting analyst data engineering reporting analyst data engineering reporting analyst Finance Product Engineering ... Business Functions
  11. 11. Highly Aligned, Loosely Coupled data engineering reporting analyst Finance data engineering data architect analyst/reporting Marketing
  12. 12. Highly Aligned, Loosely Coupled data engineering reporting analyst Finance data engineering data architect analyst/reporting Marketing
  13. 13. Freedom & Responsibility Freedom Don’t limit access Don’t limit choices Reduce constraints Responsibility Trust Don’t allow chaos Reduce accidents
  14. 14. Protected access CENTRAL DATA
  15. 15. Protected access CENTRAL DATA NOT WHAT WE DO
  16. 16. Unlocked access CENTRAL DATA
  17. 17. Don’t limit choice CENTRAL DATA OPERATIONAL DATA LOCAL DATA
  18. 18. OPERATIONAL DATA Don’t limit choice Sting CENTRAL DATA LOCAL DATA
  19. 19. Why is Tableau a good choice? Quick Intuitive Rich Visual Analysis Storytelling Emailed Reporting Reusability
  20. 20. Who uses Tableau?
  21. 21. How do we use Tableau?
  22. 22. Examples ● Application build testing ● Certification tracking ● Operational Excellence
  23. 23. Application Build Testing (1/4) ● Team: Product Engineering ● Context ○ Application automatically tested at every code checkin ○ Several dozen performance tests run to measure change and avoid regression ● Problem ○ Limited graphing tool built into test tool ○ Difficult / no customization
  24. 24. Application Build Testing (2/4) { "metadata" : { "TestCaseName" : "Trunk.Rendering.Effects_Mask2", "MarkerSetId" : 2472165, "ESN" : "DCQA01", "UIBuild" : null, "Build" : "2689", "JenkinsJob" : "http://builds.netflix.com/job/208/", "Label" : "#2689 / 208", "BuildTimestamp" : null, "Changelist" : "2177893" }, "results" : [ {"MeanFps" : 60.284862537264004}, {"MeanFps" : 60.264900662251655}, {"MeanFps" : 60.234541577825162} ] } Build / Test
  25. 25. Application Build Testing (3/4)
  26. 26. Application Build Testing (4/4)
  27. 27. Certification Tracking (1/3) Team: Certification Operations Context ● We certify the Netflix implementation on many new consumer electronics devices Problem ● Time consuming to generate insights across multiple disconnected systems
  28. 28. Certification Tracking (2/3) NTS Certification Process
  29. 29. Certification Tracking (3/3)
  30. 30. Operational Excellence (1/3) Team: Data Science & Engineering Context ● Ensure continuous development does not negatively impact availability and resilience Problem ● Multiple programs and data sources ● Need to link source data patterns to engineering tools
  31. 31. Operational Excellence (2/3)
  32. 32. Operational Excellence (3/3)
  33. 33. Where are we with Tableau?
  34. 34. DATA @ NFLX ● Netflix is known for being data driven ● Big data is available everywhere ● Our culture enables analysis everywhere ● Tableau complements our culture ● We have organic growth throughout Netflix ● Growing part of our reporting platform
  35. 35. What can we answer? Blake Irvine Manager, Device Analytics Data Science & Engineering birvine@netflix.com Albert Wong Manager, Reporting Platforms Cloud & Platform Engineering albwong@netflix.com

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