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.

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

5. 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

7. BIG
DATA

8. 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

9. How do we innovate
with Big Data?

10. TOOLS
CULTURE

11. Tools
DATA STORAGE DATA PROCESSORS DATABASE REPORTING
Sting

12. Team Structure
Data Science and Engineering
Marketing
data engineering
reporting
analyst
Finance Product Engineering ...
Business Functions

13. Team Structure
Data Science and Engineering
WE DO
NOT WHAT Marketing
data engineering
reporting
analyst
Finance Product Engineering ...

14. 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

15. Highly Aligned, Loosely Coupled
data engineering
reporting
analyst
Finance
data engineering
data architect
analyst/reporting
Marketing

16. Highly Aligned, Loosely Coupled
data engineering
reporting
analyst
Finance
data engineering
data architect
analyst/reporting
Marketing

17. Freedom & Responsibility
Freedom
Don’t limit access
Don’t limit choices
Reduce constraints
Responsibility
Trust
Don’t allow chaos
Reduce accidents

18. Protected access
CENTRAL DATA

19. Protected access
CENTRAL DATA NOT WHAT WE DO

20. Unlocked access
CENTRAL DATA

21. Don’t limit choice
CENTRAL DATA OPERATIONAL DATA
LOCAL DATA

22. OPERATIONAL DATA
Don’t limit choice
Sting
CENTRAL DATA LOCAL DATA

23. Why is Tableau a good choice?
Quick
Intuitive
Rich Visual Analysis
Storytelling
Emailed Reporting
Reusability

24. Who uses Tableau?

25. How do we use Tableau?

26. Examples
● Application build testing
● Certification tracking
● Operational Excellence

27. 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

28. 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

29. Application Build Testing (3/4)

30. Application Build Testing (4/4)

31. 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

32. Certification Tracking (2/3)
NTS
Certification
Process

33. Certification Tracking (3/3)

34. 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

35. Operational Excellence (2/3)

36. Operational Excellence (3/3)

37. Where are we with
Tableau?

38. 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

39. 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