Big Data Ecosystem at InMobi, Nasscom ATC 2013 Noida
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This document discusses InMobi's big data ecosystem. It describes how InMobi leverages big data through various data science-driven systems for optimization, prediction, recommendation, targeting, and pricing. These systems include Conduit and PinTail for data ingestion, Falcon for data management, Grill for analytics, and Storm for real-time processing. It also provides metrics on InMobi's large Hadoop cluster and volume of data processed daily. The goal of InMobi's data platform is to commoditize data access and processing by providing rich abstractions to power products and drive business decisions.
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Big Data Ecosystem at InMobi, Nasscom ATC 2013 Noida
- 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