How Stranger Things can happen with Visual Analytics Jason Flittner - - PowerPoint PPT Presentation

▶
how stranger things can
SMART_READER_LITE
LIVE PREVIEW

How Stranger Things can happen with Visual Analytics Jason Flittner - - PowerPoint PPT Presentation

Dec 24, 2022 328 likes •588 views

#NetflixData How Stranger Things can happen with Visual Analytics Jason Flittner Senior Analytics Engineer / Manager Netflix - Content Data Engineering and Analytics About Netflix Tableau + Big Data Lessons Learned Where

slide-1
SLIDE 1

How “Stranger Things” can happen with Visual Analytics

Jason Flittner Senior Analytics Engineer / Manager Netflix - Content Data Engineering and Analytics

#NetflixData

slide-2
SLIDE 2
  • About Netflix
  • Tableau + Big Data

○ Lessons Learned ○ Where we are today

  • Analytics and Iterating Quickly
slide-3
SLIDE 3

What is Netflix?

slide-4
SLIDE 4
slide-5
SLIDE 5
slide-6
SLIDE 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

slide-7
SLIDE 7
  • ~60 PB DW on S3
  • ~1400 Tableau users
  • Live & extract connections
  • Analytics on billions of rows
slide-8
SLIDE 8

(Hadoop clusters)

Storage Compute Data Interface Data Access, Analytics and Visualization

AWS S3

slide-9
SLIDE 9
  • About Netflix
  • Tableau + Big Data

○ Lessons Learned ○ Where we are today

  • Analytics and Iterating Quickly
slide-10
SLIDE 10

Choosing a source

  • Hive
  • Spark
  • Presto
  • Redshift
  • Published Data Source
  • etc...
slide-11
SLIDE 11
  • Powerful and scalable backend
  • “Slower” 1,000,000,000/hr
  • Hive + Tableau

○ Thrift Servers ○ Custom SQL vs Tables ○ Metadata ○ ODBC Optimization

slide-12
SLIDE 12
  • Scalable
  • Faster than Hive in many cases
  • Spark + Tableau

○ Thrift Servers ○ Long running job on Cluster ○ Query reliability

slide-13
SLIDE 13
  • Fast query engine
  • Great for experimenting and

“smaller” data sets

  • Connecting to Tableau

○ Web data connector ○ ODBC

slide-14
SLIDE 14
  • About Netflix
  • Tableau + Big Data

○ Lessons Learned ○ Where we are today

  • Analytics and Iterating Quickly
slide-15
SLIDE 15

Tableau Data Extract Publish to Server

Tableau Extract API

slide-16
SLIDE 16

Create Tableau Data Extract Provision Container Resource Issues Command Create Extract Publish to Server

Distributed Tableau Extract API

slide-17
SLIDE 17
  • Very fast loads from S3
  • Native Tableau connector
  • Quick Tableau Iteration
  • Live or Extract
  • Concurrency

Amazon Redshift

slide-18
SLIDE 18

BIG Data

  • Too big to extract?
  • Optimized live connections

○ SQL

  • Custom data viz with Druid
  • Tableau + Hyper!?
slide-19
SLIDE 19
  • About Netflix
  • Tableau + Big Data

○ Lessons Learned ○ Where we are today

  • Analytics and Iterating Quickly
slide-20
SLIDE 20

Business users Analytics Engineer

Analytics:

  • Binge Analysis
  • Viewing Patterns
  • Hours Viewed
  • Customer Joy
  • Content Quality
slide-21
SLIDE 21

Bringing it all together

  • Content analytics
  • Iterate quickly
  • Move between backend sources
  • Strong user adoption
slide-22
SLIDE 22
slide-23
SLIDE 23
slide-24
SLIDE 24
slide-25
SLIDE 25

Merci Thank you

Jason Flittner -