Spark Summit East Talk

1. Building a Just-In-Time Data
Warehouse
Dan Morris (Viacom)
JasonPohl (Databricks)
February 18, 2016

2. About Viacom
• LeadingGlobalEntertainmentContentCompany
• 23 Brandsin 170+ Countries
2

3. Introductions
Dan Morris
• SeniorDirectorof ProductAnalytics
• 12 Years @Viacomin a varietyof roles
• Intersectionof ProductandData
3

4. About My Team
• ProductAnalytics team formedone year ago
• Ourmissionis to grow our globalaudiencewiththe
highestquality userspossible
4

5. Key Areas of Focus
• Mobilizeeffortsusing growthtargets
• Uncoverdeepinsightsusing churnand cohort
analysis
• Treat all ideas as hypothesesand test them
rigorously
5

6. Where Are We Today: App Platform
6
Make it extremelysimpleto build and deploy engaging
apps around the globe
FEATURES
UI & ANIMATIONS
CONFIGURATION SETTINGS
APP BINARY

7. Disciplined Product Dev Approach is Key
7
• 23 brandsin 170+ countries
• Lots of marketdynamics
• Many stakeholders
... Data is a must!

8. Sound Data Management is Required
8
9
11
13
14
16
18
Jan Feb Mar Apr May Jun Jul Aug Sep Oct Nov Dec
Expected Data Volume Growth (TB)
2016

9. Our Data Infrastructure
9
S3
Spark +
Databricks
Redshift Tableau

10. Introducing the TV Land iOS App
10

11. Applying Product Analytics to TV Land
11
• Growth Targets
• Dashboards
• DeepDive Analyses
• A/B Testing

12. Baselines Used to Set Growth Targets
12
Business ModelingETL
Data Volume
• 30 sites/apps
• 11 TB
Data Volume
• 30 sites/apps
• 1 TB
S3
Spark +
Databricks
Redshift Tableau

13. Growth Targets are Monitored via Dashboards
13
1/4/16 1/11/16 1/18/16 1/25/16
New Users
Returning Users
Weekly Retention by Cohort
0 1 2 3 4
1/4/16 100% 53% 41% 33% 30%
1/11/16 100% 58% 51% 42%
1/18/16 100% 49% 38%
1/25/16 100% 49%
Audience Growth by Cohort

14. Dashboards Spark Deep Dive Analyses
14
0%
25%
50%
75%
100%
0 1 2 3 4 5 6 7 8 9 10 11 12
Users will quickly churn if
not activated within small
window of time.
%ofusersretained

15. Deep Dive Analysis Requires Flexibility
15
Not NeededDeep Dive Analysis
S3
Spark +
Databricks
• Define schema on read instead of write
• Work through data quality issues just-in-time.
• Tease out business question iterative and interactively.
• Use programming language of your choice.
Redshift Tableau

16. Hypotheses Require A/B Testing
16
Statistical Analysis
Data Sets
• Adobe Logs
• Experiment Logs
S3
Spark +
Databricks
Tableau
Not Needed
Redshift Tableau

17. Summary of Our Setup
17
Just in Time Traditional
Primary Audience • Product Analysts • Product Team
• Business
Stakeholders
Tasks • Exploratory
Analysis
• A/B Testing
• Ad Hoc Queries
• Dashboards
Tools • S3
• Spark
• Databricks
• Redshift
• Tableau

18. Coming Soon…
18
• Go live with internalA/B testing platform
• Continueto evolve our setup
• Further scale model to support Product AnalyticsPan-
Viacom

19. Questions ?
19

20. Thank you.
Otherpartingwordsorcontactinformationgohere.