Michelle Ufford is a Principal Architect at Netflix who leads their Data Engineering and Analytics team. Netflix has over 86 million members who watch over 125 million hours of content daily on over 1000 supported devices. The data team manages a 40 petabyte data warehouse with 4 petabyte daily reads and 300 terabyte daily writes, processing over 700 billion events. The data is used to predict content value, optimize the user experience, analyze news and PR, monitor global service delivery, and power experimentation.

1. Data-Driven @ Netflix
Michelle Ufford
Principal Architect
Data Engineering & Analytics

2. Michelle Ufford
Highlights
● Principal Architect at Netflix
Data Engineering & Analytics
● Prev. Engineering Manager at GoDaddy
Data Platform
● Microsoft Data Platform MVP
● 10+ years building web-scale analytics &
data engineering infrastructure
● advises on Big Data topics
Microsoft, Hortonworks, Teradata, etc.
Gratuitous picture of my kids

3. By the Numbers.

4. The business numbers.
86.7 million
members
1000+ devices
supported
125+ million
hours watched
launched
19 years ago
every. day.

5. Any device. Anywhere.*
* Well, almost anywhere.

6. The data numbers.
4 petabyte
DW reads
300 terabyte
DW writes
40 petabyte
data warehouse
700+ billion
events written

7. Data in Action.

8. Content.
What should we license?

9. Predicting Value for
Licensed Content.
Feature Engineering Predictive Models License Terms Content Efficiency

10. Predicting Value for
Licensed Content.
● past performance of similar content on Netflix
● broadcast & Box Office performance
● talent (writers, actors, directors, etc.)
● critic & user reviews
● awards & accolades
Feature Engineering Predictive Models License Terms Content Efficiency

11. Predicting Value for
Licensed Content.
Feature Engineering Predictive Models License Terms Content Efficiency

12. Predicting Value for
Licensed Content.
● terms (length, exclusivity, etc.)
● bid amount
● negotiations
Feature Engineering Predictive Models License Terms Content Efficiency

13. Predicting Value for
Licensed Content.
● value / cost
● if efficient, license
Feature Engineering Predictive Models License Terms Content Efficiency

14. “ last year our original content overall
was some of our most efficient content.
”

15. “ We are building a studio in the cloud
and pioneering new approaches to movie
production, optimizing pitches, production
schedules, subtitling, and digital asset
management for our Original content. ”

17. What should we license?create?

18. Product UX.
Data. Driven. Experience.

24. There are 86 million different
versions of Netflix.

25. billboard
rows, row order
titles, title order
title artwork

26. Public Relations.
Analytics of news.Analytics is news.

29. Content Delivery.
Monitoring a global service.

31. YouTube video of Vizceral demo
https://youtu.be/JctsPpgEsVs

32. Behind the Scenes.

33. data access
AWS
S3
Big Data Platform
Amazon
Redshift
data processing
fast storage data viz
METACA
T
data services
events data
operational data
elastic storage Apache Pig

34. Philosophy.

36. Freedom &
Responsibility.

38. Context,
Not Control.

41. Highly Aligned &
Loosely Coupled.

42. Big Data
Platform

43. Data Engineering & Analytics
MarketingProduct PlaybackContent Finance
105 talented engineers & analysts
data viz engineers
analytics engineers
data engineers
Big Data
Platform
analysts

44. Results,
Not Opinions.

48. Experimentation Platform
Batch &
Ad Hoc
Analysis

49. Questions?

50. Thank you
for attending!
Michelle Ufford
linkedin.com/in/mufford
@sqlfool
Data @
Netflix
@NetflixData
hadoopsie.com techblog.netflix.com
tinyurl.com/NetflixData