Just a copy from http://www.isim.ac.in/Infovision%202012/presentations/sunilshirguppilinkedin.pdf for saving purpose. All right reserved by the above link.

1. Big Data EcoSystem @ LinkedIn
October 20, 2012
LinkedIn Confidential ©2013 All Rights Reserved

2. LinkedIn Confidential ©2013 All Rights Reserved
Sunil Shirguppi
Head of Data Services- International
LinkedIn Corporation
http://www.linkedin.com/in/sunilshirguppi

3. Outline
LinkedIn Overview
Data Science
Big Data Eco-System
Learnings
LinkedIn Confidential ©2013 All Rights Reserved 3

4. Our Mission
Connect the world’s professionals
to make them more productive and successful
LinkedIn Confidential ©2013 All Rights Reserved 4

5. We are the professional profile of record
Googled yourself lately?
Don’t feel bad, we all do it.

6. Executives from all
Companies are
LinkedIn members

7. The LinkedIn Opportunity
LinkedIn Confidential ©2013 All Rights Reserved 7
Fundamentally transforming the way the world worksFundamentally transforming the way the world works
Connect talent with opportunity at massive scale
+

8. The World’s Largest Professional Network
LinkedIn Confidential ©2013 All Rights Reserved 8
*as of Nov 4, 2011
**as of June 30, 2011
2
4
8
17
32
55
90
2004 2005 2006 2007 2008 2009 2010
LinkedIn Members (Millions)
175M+*
82%
Fortune 100 Companies
use LinkedIn to hire
Company Pages
>2M
**
New Members joining
~2/sec
Professional
searches in 2011
~4.2B

9. Multiple revenue channels
 Premium Subscriptions
 Self Serve Ads
 Hiring Solutions
 Marketing Solutions

10. Let’s talk Data…

11. Business is recognizing the importance of analytics

12. Data Scientist = Curiosity + Intuition + Data
gathering + Standardization + Statistics + Modeling
+ Visualization + Communication
What makes a Data Scientist?

13. Big Data at LinkedIn
LinkedIn Confidential ©2013 All Rights Reserved 13
* Chart from Philip Russom- Research Director: TDWI

14. What do we do with Data?
 Data Standardization
 Build innovative data products to help professionals
 Draw insights
 Drive the business
Before we can do that...
There are a few challenges that we have to overcome
• Scale
• Standardization
• Infrastructure

15. Few Data-Driven Products
LinkedIn Confidential ©2013 All Rights Reserved 15
Pandora Search for People
Events You
May Be
Interested In
Groups browse maps

16. How do we do it?

18. LinkedIn Sample Data Stack
Crowdsourcing

19. Big Data at LinkedIn
LinkedIn Confidential ©2013 All Rights Reserved 19
Users
Online Data
Store
Near-Line
Data Store
Application Offline Data
Store
Web
Logs
High-level data environment
Challenges so complex which
off-the-shelf or a few
technologies can’t address
Built our own combination of
toolsets/ technologies to
meet specific requirements

20. LinkedIn Data Stack – Online
LinkedIn Confidential ©2013 All Rights Reserved 20
Users
Online Data
Store
Near-Line
Data Store
Application Offline Data
Store
Web
Logs
Systems Capabilities
• Rich structures (e.g. indexes)
• Change capture capability

21. LinkedIn Data Stack – Nearline
LinkedIn Confidential ©2013 All Rights Reserved 21
Users
Online Data
Store
Near-Line
Data Store
Application Offline Data
Store
Web
Logs
Systems Capabilities
• Key value accessVoldemort
• Search platform
• Distributed Graph engine
Zoie Bobo Sensei
D-Graph

22. LinkedIn Data Stack – Pipeline
LinkedIn Confidential ©2013 All Rights Reserved 22
Users
Online Data
Store
Near-Line
Data Store
Application Offline Data
Store
Web
Logs
Systems Capabilities
• Messaging for site events,
monitoring
• Change data capture streams

23. LinkedIn Data Stack – Offline
LinkedIn Confidential ©2013 All Rights Reserved 23
Users
Online Data
Store
Near-Line
Data Store
Application Offline Data
Store
Web
Logs
Systems Capabilities
• Machine learning, ranking,
relevance
• Warehouse and analytics

24. LinkedIn with Hadoop, Aster, and Teradata
LinkedIn Confidential ©2013 All Rights Reserved 24
Integrated Data
Warehouse
• Exec Dashboards
• Adhoc/OLAP
• Complex SQL
• SQL
Data transformation
& batch processing
• Image processing
• Search indexes
• Graph (PYMK)
• MapReduce
Analytic Platform for data
discovery
• nPath Pattern/Path
• Clickstream analysis
• A/B site testing
• Data Sciences discovery
• SQL-MapReduce
Aster/Teradata
Bi-Directional Connector
Aster/Teradata
Hadoop Connectors
Batch data transformations for
engineering groups using HDFS +
MapReduce
Batch data transformations for
engineering groups using HDFS +
MapReduce
Interactive MapReduce
analytics for the enterprise using
MapReduce Analytics &
SQL-MapReduce
Interactive MapReduce
analytics for the enterprise using
MapReduce Analytics &
SQL-MapReduce
Integration with structured data,
operational intelligence, scalable
distribution of analytics
Integration with structured data,
operational intelligence, scalable
distribution of analytics

26. It’s a global economy
Country connectedness on LinkedIn

27. Data deep dives
Job migration after financial collapse

28. How Often do people change jobs?

29. Visualization is important

30. If your name is Chip, you are likely in sales!

31. 31
Industry Growth

32. Buzzwords

33. What next?
• Self service analytics
• Metadata framework
• Integrate reporting solutions
• Go Mobile!
• Scalability and Data Quality

34. Challenges
• Data volumes and availability
– Billion+ rows every day
– Users in Global locations need data
• Multiple platforms
– Agile development
– Data Integration
 Data Quality
– User input data
– Data standardization

35. Key Learnings
 Self Service
– Making data accessible to key stakeholders in a timely
manner creates tremendous value.
– Viz is more important than we think
• Measuring your future investments
– Performance is not the only measure
– Company fundamentals matter
• As an Data team, be in control of your destiny
– Identify what to measure and lead by metrics
– Become the Think-tank

36. Web 3.0 – It’s all about data!!
LinkedIn Confidential ©2013 All Rights Reserved 36

37. ULTIMATELY…

38. It is all about the people!

39. LinkedIn Confidential ©2013 All Rights Reserved 39
Thank You!