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Data-Driven @ Netflix
Michelle Ufford
Principal Architect
Data Engineering & Analytics
Michelle Ufford
Highlights
● Principal Architect at Netflix
Data Engineering & Analytics
● Prev. Engineering Manager at Go...
By the Numbers.
The business numbers.
86.7 million
members
1000+ devices
supported
125+ million
hours watched
launched
19 years ago
every....
Any device. Anywhere.*
* Well, almost anywhere.
The data numbers.
4 petabyte
DW reads
300 terabyte
DW writes
40 petabyte
data warehouse
700+ billion
events written
Data in Action.
Content.
What should we license?
Predicting Value for
Licensed Content.
Feature Engineering Predictive Models License Terms Content Efficiency
Predicting Value for
Licensed Content.
● past performance of similar content on Netflix
● broadcast & Box Office performan...
Predicting Value for
Licensed Content.
Feature Engineering Predictive Models License Terms Content Efficiency
Predicting Value for
Licensed Content.
● terms (length, exclusivity, etc.)
● bid amount
● negotiations
Feature Engineering...
Predicting Value for
Licensed Content.
● value / cost
● if efficient, license
Feature Engineering Predictive Models Licens...
“ last year our original content overall
was some of our most efficient content.
”
“ We are building a studio in the cloud
and pioneering new approaches to movie
production, optimizing pitches, production
...
What should we license?create?
Product UX.
Data. Driven. Experience.
There are 86 million different
versions of Netflix.
billboard
rows, row order
titles, title order
title artwork
Public Relations.
Analytics of news.Analytics is news.
Content Delivery.
Monitoring a global service.
YouTube video of Vizceral demo
https://youtu.be/JctsPpgEsVs
Behind the Scenes.
data access
AWS
S3
Big Data Platform
Amazon
Redshift
data processing
fast storage data viz
METACA
T
data services
events d...
Philosophy.
Freedom &
Responsibility.
Context,
Not Control.
Highly Aligned &
Loosely Coupled.
Big Data
Platform
Data Engineering & Analytics
MarketingProduct PlaybackContent Finance
105 talented engineers & analysts
data viz engineers...
Results,
Not Opinions.
Experimentation Platform
Batch &
Ad Hoc
Analysis
Questions?
Thank you
for attending!
Michelle Ufford
linkedin.com/in/mufford
@sqlfool
Data @
Netflix
@NetflixData
hadoopsie.com techbl...
Data-Driven @ Netflix
Data-Driven @ Netflix
Data-Driven @ Netflix
Data-Driven @ Netflix
Data-Driven @ Netflix
Data-Driven @ Netflix
Data-Driven @ Netflix
Data-Driven @ Netflix
Data-Driven @ Netflix
Data-Driven @ Netflix
Data-Driven @ Netflix
Data-Driven @ Netflix
Data-Driven @ Netflix
Data-Driven @ Netflix
Data-Driven @ Netflix
Data-Driven @ Netflix
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Data-Driven @ Netflix

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Slides from Michelle Ufford's talk, Data-Driven @ Netflix. Talk given at PASS Summit 2016 in October 2016.

Netflix is the quintessential data-driven company. It’s 83 million members stream more than 125 million hours in over 190 countries every day and generate more than 700 billion events in the process. In this session, we’ll share how data is used to make informed decisions across the entire business — from content acquisition to content delivery, and everything in between. We’ll look at how Netflix successfully employs a scalable cloud-based data platform to support a constant deluge of data and a small army of data analysts, engineers, and scientists. We’ll discuss the advanced analytical capabilities that are enabled through modern data technologies. Lastly, we’ll explore some of the architectural & operational principals that enable Netflix to so effectively make use of its data.

Veröffentlicht in: Technologie
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Data-Driven @ Netflix

  1. 1. Data-Driven @ Netflix Michelle Ufford Principal Architect Data Engineering & Analytics
  2. 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. 3. By the Numbers.
  4. 4. The business numbers. 86.7 million members 1000+ devices supported 125+ million hours watched launched 19 years ago every. day.
  5. 5. Any device. Anywhere.* * Well, almost anywhere.
  6. 6. The data numbers. 4 petabyte DW reads 300 terabyte DW writes 40 petabyte data warehouse 700+ billion events written
  7. 7. Data in Action.
  8. 8. Content. What should we license?
  9. 9. Predicting Value for Licensed Content. Feature Engineering Predictive Models License Terms Content Efficiency
  10. 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. 11. Predicting Value for Licensed Content. Feature Engineering Predictive Models License Terms Content Efficiency
  12. 12. Predicting Value for Licensed Content. ● terms (length, exclusivity, etc.) ● bid amount ● negotiations Feature Engineering Predictive Models License Terms Content Efficiency
  13. 13. Predicting Value for Licensed Content. ● value / cost ● if efficient, license Feature Engineering Predictive Models License Terms Content Efficiency
  14. 14. “ last year our original content overall was some of our most efficient content. ”
  15. 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. ”
  16. 16. What should we license?create?
  17. 17. Product UX. Data. Driven. Experience.
  18. 18. There are 86 million different versions of Netflix.
  19. 19. billboard rows, row order titles, title order title artwork
  20. 20. Public Relations. Analytics of news.Analytics is news.
  21. 21. Content Delivery. Monitoring a global service.
  22. 22. YouTube video of Vizceral demo https://youtu.be/JctsPpgEsVs
  23. 23. Behind the Scenes.
  24. 24. 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
  25. 25. Philosophy.
  26. 26. Freedom & Responsibility.
  27. 27. Context, Not Control.
  28. 28. Highly Aligned & Loosely Coupled.
  29. 29. Big Data Platform
  30. 30. Data Engineering & Analytics MarketingProduct PlaybackContent Finance 105 talented engineers & analysts data viz engineers analytics engineers data engineers Big Data Platform analysts
  31. 31. Results, Not Opinions.
  32. 32. Experimentation Platform Batch & Ad Hoc Analysis
  33. 33. Questions?
  34. 34. Thank you for attending! Michelle Ufford linkedin.com/in/mufford @sqlfool Data @ Netflix @NetflixData hadoopsie.com techblog.netflix.com tinyurl.com/NetflixData

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