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How “Stranger Things” can happen with Visual Analytics
Jason FlittnerSenior Analytics Engineer / ManagerNetflix - Content Data Engineering and Analytics
#NetflixData
● About Netflix
● Tableau + Big Data
○ Lessons Learned
○ Where we are today
● Analytics and Iterating Quickly
What is Netflix?
● 93+ million members
● 190 countries
● 1,000+ devices
● 10B hours/qtr
We plan on spending ~$6B in 2017 on content for our members
Metrics
● ~60 PB DW on S3
● ~1400 Tableau users
● Live & extract connections
● Analytics on billions of rows
(Hadoop clusters)
Storage Compute Data Interface Data Access, Analytics and Visualization
AWS S3
● About Netflix
● Tableau + Big Data
○ Lessons Learned
○ Where we are today
● Analytics and Iterating Quickly
Choosing a source
● Hive
● Spark
● Presto
● Redshift
● Published Data Source
● etc...
● Powerful and scalable backend
● “Slower” 1,000,000,000/hr
● Hive + Tableau
○ Thrift Servers
○ Custom SQL vs Tables
○ Metadata
○ ODBC Optimization
● Scalable
● Faster than Hive in many cases
● Spark + Tableau
○ Thrift Servers
○ Long running job on Cluster
○ Query reliability
● Fast query engine
● Great for experimenting and
“smaller” data sets
● Connecting to Tableau
○ Web data connector
○ ODBC
● About Netflix
● Tableau + Big Data
○ Lessons Learned
○ Where we are today
● Analytics and Iterating Quickly
Tableau Data Extract Publish to Server
Tableau Extract API
Create Tableau Data ExtractProvision Container ResourceIssues Command Create Extract
Publish to Server
Distributed Tableau Extract API
● Very fast loads from S3
● Native Tableau connector
● Quick Tableau Iteration
● Live or Extract
● Concurrency
Amazon Redshift
BIG Data● Too big to extract?
● Optimized live connections
○ SQL
● Custom data viz with Druid
● Tableau + Hyper!?
● About Netflix
● Tableau + Big Data
○ Lessons Learned
○ Where we are today
● Analytics and Iterating Quickly
Business users
Analytics Engineer
Analytics:
● Binge Analysis
● Viewing Patterns
● Hours Viewed
● Customer Joy
● Content Quality
Bringing it all together
● Content analytics
● Iterate quickly
● Move between backend sources
● Strong user adoption
Merci
Thank you
Jason Flittner -