The document discusses Netflix's use of visual analytics with Tableau and big data to enhance content data engineering and analytics. It details their massive data infrastructure, the tools employed (like Hive, Spark, and Presto), and how they analyze user metrics to improve content delivery and customer experience. Key themes include the importance of scalability, speed in analytics, and strong user adoption to iterate quickly on insights.

1. How “Stranger Things” can
happen with Visual
Analytics
Jason Flittner
Senior Analytics Engineer / Manager
Netflix - Content Data Engineering and Analytics
#NetflixData

2. ● About Netflix
● Tableau + Big Data
○ Lessons Learned
○ Where we are today
● Analytics and Iterating Quickly

3. What is Netflix?

6. ● 93+ million members
● 190 countries
● 1,000+ devices
● 10B hours/qtr
We plan on spending ~$6B in 2017 on
content for our members
Metrics

7. ● ~60 PB DW on S3
● ~1400 Tableau users
● Live & extract connections
● Analytics on billions of rows

8. (Hadoop
clusters)
Storage Compute Data Interface Data Access, Analytics and Visualization
AWS
S3

9. ● About Netflix
● Tableau + Big Data
○ Lessons Learned
○ Where we are today
● Analytics and Iterating Quickly

10. Choosing a source
● Hive
● Spark
● Presto
● Redshift
● Published Data Source
● etc...

11. ● Powerful and scalable
backend
● “Slower” 1,000,000,000/hr
● Hive + Tableau
○ Thrift Servers
○ Custom SQL vs Tables
○ Metadata
○ ODBC Optimization

12. ● Scalable
● Faster than Hive in many
cases
● Spark + Tableau
○ Thrift Servers
○ Long running job on
Cluster
○ Query reliability

13. ● Fast query engine
● Great for experimenting and
“smaller” data sets
● Connecting to Tableau
○ Web data connector
○ ODBC

14. ● About Netflix
● Tableau + Big Data
○ Lessons Learned
○ Where we are today
● Analytics and Iterating Quickly

15. Tableau Data
Extract Publish to Server
Tableau Extract
API

16. Create Tableau Data ExtractProvision Container ResourceIssues Command Create
Extract
Publish to Server
Distributed Tableau Extract API

17. ● Very fast loads from S3
● Native Tableau connector
● Quick Tableau Iteration
● Live or Extract
● Concurrency
Amazon
Redshift

18. BIG Data ● Too big to extract?
● Optimized live connections
○ SQL
● Custom data viz with Druid
● Tableau + Hyper!?

19. ● About Netflix
● Tableau + Big Data
○ Lessons Learned
○ Where we are today
● Analytics and Iterating Quickly

20. Business
users
Analytics
Engineer
Analytics:
● Binge Analysis
● Viewing Patterns
● Hours Viewed
● Customer Joy
● Content Quality

21. Bringing it all
together
● Content analytics
● Iterate quickly
● Move between backend sources
● Strong user adoption

25. Merci
Thank you
Jason Flittner -