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Harvesting Information Excellence

Information Excellence2012 Sep Session

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Big Data Usage and Implementation in Linkedin

Hari Shankar, Big Data Engineer, Linkedin

Thank You

for hosting us today

Today’s Speakers

Big data and Hadoop

September 2012

Hari Shankar MenonSoftware engineerLinkedIn

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LinkedIn Engineering Data warehouse team

Previously, Software engineer @Clickable– Worked on building the reporting and analytics platform on

Hadoop and HBase.

Hadoop and Open-source enthusiast

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About me

About LinkedIn Data Infrastructure overview Hadoop@LinkedIn Challenges

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Agenda

Our missionConnect the world’s professionals to make

them more productive and successful

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*as of Nov 4, 2011**as of June 30, 2011

2 48

17

32

55

90

2004 2005 2006 2007 2008 2009 2010

LinkedIn Members (Millions)

175M+

85%Fortune 100 Companies use LinkedIn to hire

Company Pages

>2M

**

New Members joining

~2/sec

Professional searches in 2011

~4.2B

LinkedIn by numbers

About LinkedIn Data Infrastructure overview Hadoop@LinkedIn Challenges

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* Chart from Philip Russom- Research Director: TDWI

What is big data?

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Infrastructure technologies

Primary data store (Front-end)Distributed key-value store

Document-oriented store

Distributed PubSub messaging

Search technologies

Database change replication SenseiDB

Zoie Bobo

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http://data.linkedin.com/opensource

Open source

About LinkedIn Data Infrastructure overview Hadoop@LinkedIn Challenges

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What is Hadoop Evolution of Hadoop Impact

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Recommendation systems– Generating recommendations– Modeling– A/B Testing– Grandfathering

Data warehouse/ETL– Raw data storage– Aggregations– Heavy lifting

Data sciences– Strategic analyses– Experimentation sandbox

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@

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Pandora Search for People

Events YouMay BeInterested In

Groups browse maps

The Recommendations opportunity

• Relevance/Latency

• Offline computation

• Caching

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Improving recommendations

• Mathematical modeling

• A/B Testing

• Grandfathering

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Hadoop in the Data warehouse

• Source of truth• Lower retention• Ad-hoc analysis

• Longer retention• Complex

transformations• Algorithmic

computations

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Hadoop in Data Sciences

• Deep dives

• Sandbox

• Hackday projects

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Data Insights - 1

Job migration after financial collapse

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Data Insights - 2

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Data Insights - 3

About LinkedIn Data Infrastructure overview Hadoop@LinkedIn Challenges

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1. User adoption of new technologies2. Real-time processing3. Graph/Network algorithms4. Making data accessible

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Challenges

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User adoption

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• Challenges• Random reads/writes• Warm-up time

• Solutions• Parts of the problem that can be moved offline?• HBase, Voldemort

Real-time processing

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• Graph problems• Traditional joins

Map-reduce-incompatible problems

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• Hadoop Tons of data

Making data accessible

Finally!

No Silver bullet

Hadoop Offline processing

Scalability by design

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www.linkedin.com/in/harisreekumar

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www.linkedin.com/company/linkedin/careers

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