Your SlideShare is downloading. ×
NATC 2013 - Big Data Ecosystem at InMobi by Sharad Agarwal, InMobi
Upcoming SlideShare
Loading in...5
×

Thanks for flagging this SlideShare!

Oops! An error has occurred.

×
Saving this for later? Get the SlideShare app to save on your phone or tablet. Read anywhere, anytime – even offline.
Text the download link to your phone
Standard text messaging rates apply

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

372

Published on

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

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

Published in: Technology
0 Comments
0 Likes
Statistics
Notes
  • Be the first to comment

  • Be the first to like this

No Downloads
Views
Total Views
372
On Slideshare
0
From Embeds
0
Number of Embeds
1
Actions
Shares
0
Downloads
20
Comments
0
Likes
0
Embeds 0
No embeds

Report content
Flagged as inappropriate Flag as inappropriate
Flag as inappropriate

Select your reason for flagging this presentation as inappropriate.

Cancel
No notes for slide

Transcript

  • 1.  BIG  DATA  ECOSYSTEM  AT   INMOBI   Sharad  Agarwal   Sharad Agarwal Nasscom ATC 2013
  • 2. Technology and Product have led to InMobi being recognized by MIT as one of the Top 50 Disruptive Companies for 2013 2  
  • 3. InMobi Global Reach And Scale 3  
  • 4. 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  
  • 5. 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
  • 6. 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
  • 7. 1 Access  to  Data   2 Ability  to  Process   3 Ability  to  U@lize   7  
  • 8. Curate Reporting & Analytics Ingest Data Ingestion Normalize Data Systems Analyze Store Data Flow Data Consumption Feedback -> To power products 8
  • 9. Commoditize Data Access And Processing By Providing Rich Abstractions Design: Data Platform Goal 9
  • 10. 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  
  • 11. Collect signals – streaming, batch, multi-site At Scale In Real Time Conduit + PinTail 1 1  
  • 12. 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  
  • 13. InMobi Incubated Its Hadoop Data Management Project in Apache Apache Falcon 1 3  
  • 14. Apache Falcon
  • 15. Adhoc Reporting on Logical Cube Abstraction Across Heterogeneous Storages GRILL 1 5  
  • 16. GRILL: Query on Cube using HQL 1 6  
  • 17. 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
  • 18.   sharad@apache.org   @sharad_ag     Bangalore  Hadoop   Meetup   Thank You 18  

×