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NATC 2013 - Big Data Ecosystem at InMobi by Sharad Agarwal, InMobi
 

NATC 2013 - Big Data Ecosystem at InMobi by Sharad Agarwal, InMobi

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NATC 2013 - Big Data Ecosystem at InMobi by Sharad Agarwal, InMobi

NATC 2013 - Big Data Ecosystem at InMobi by Sharad Agarwal, InMobi

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    NATC 2013 - Big Data Ecosystem at InMobi by Sharad Agarwal, InMobi NATC 2013 - Big Data Ecosystem at InMobi by Sharad Agarwal, InMobi Presentation Transcript

    •  BIG  DATA  ECOSYSTEM  AT   INMOBI   Sharad  Agarwal   Sharad Agarwal Nasscom ATC 2013
    • Technology and Product have led to InMobi being recognized by MIT as one of the Top 50 Disruptive Companies for 2013 2  
    • InMobi Global Reach And Scale 3  
    • Data  Sciences   Decision Making by Machines Infrastructure  Scaling   Decision Making By Humans Reports Agile Reports & Analytics Increasing Value Data Driven Business Decisions Leveraging Data Data Driven Systems 4  
    • Optimization §  §  Campaign Delivery Marketplace Health Business Metrics §  §  §  Adoption Metrics Product Performance Metrics and Debugging Planning and Strategy – Demand, Supply and others Exploration of new opportunities §  New Product / Feature Ideas Data Driven Decision Making
    • Prediction Prediction §  §  §  Prediction of Click through Rates and Conversion Rates Forecasting and Planning – Inventory / Burn Risk Mitigation and Management – Overburn / Fraud Recommendation Recommendation §  §  §  App Recommendation Engine Dynamic Personalization of Creatives Bid Budget Recommendation Targeting §  §  §  §  Audience Segment based Targeting Geo and Hyper local Targeting Contextual Targeting Look Alike Modelling Pricing §  §  §  Conversion Based Pricing Engagement based Pricing Determining the value of Supply Data Sciences Driven Systems 6
    • 1 Access  to  Data   2 Ability  to  Process   3 Ability  to  U@lize   7  
    • Curate Reporting & Analytics Ingest Data Ingestion Normalize Data Systems Analyze Store Data Flow Data Consumption Feedback -> To power products 8
    • Commoditize Data Access And Processing By Providing Rich Abstractions Design: Data Platform Goal 9
    • APLICATIONS   DASHBOARD   SDK   DATA  INGESTION     CONDUIT  +  PINTAIL     DATA  MGMT     FALCON         ANALYTICS     GRILL   Signals   Ac3onable   Insights   InMobi  Big  Data  Pla=orms   STORM   Hosted/On-­‐Premise    Cloud(Public/Private)   DATA   INFRASTRUCTURE   Server   Infrastructure  
    • Collect signals – streaming, batch, multi-site At Scale In Real Time Conduit + PinTail 1 1  
    • DC1  Producers   A_part1   B_part1   DC2  Producers   A_part2   DC3  Producers   B_part3   Control  Flow   A   DC1  Consumers   B   DC2  Consumers   A   B   Data  Flow   DC3  Consumers  
    • InMobi Incubated Its Hadoop Data Management Project in Apache Apache Falcon 1 3  
    • Apache Falcon
    • Adhoc Reporting on Logical Cube Abstraction Across Heterogeneous Storages GRILL 1 5  
    • GRILL: Query on Cube using HQL 1 6  
    • 8 Bn 240 TB Hbase Read-Write throughputs per day Amount of data read / written by systems in a day 1+ PB Storage 10 Bn Hadoop cluster 175 K Raw events per day Hadoop Jobs per day InMobi and Big Data – Metrics 17
    •   sharad@apache.org   @sharad_ag     Bangalore  Hadoop   Meetup   Thank You 18