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Razorfish - Amazon EMR usecase

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How Razorfish enabled a large Enterprse company to use Amazon EMR which increased their Return on Advertising spend by 500%

How Razorfish enabled a large Enterprse company to use Amazon EMR which increased their Return on Advertising spend by 500%

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  • Return on advertising spend (ROAS)

Razorfish - Amazon EMR usecase Razorfish - Amazon EMR usecase Presentation Transcript

  • Edge in the cloud
    Salim Hemdani
    VP, Experiences and Platforms
    @shemdani
  • 500,000,000,000
  • 1,000
  • 100
  • 25
  • 13
  • What are these numbers?
  • Numbers
    500,000,000,000 records
    1,000 clients
    100 markets
    25 data sources
    13 terabytes per day
  • Agenda
  • Time for a change
  • Transition Service Agreement
    Move from Atlas
    • Traditional hosting environment
    • Heavy on CAPEX
    • Managed by Atlas/MSFT networking teams
    • To be completed by October 2010; no interruption in SLA
    Move away from PVM
  • Ad Serving Event Log
    Request
    hash(key) mod R
    FS01
    FS03
    FS02
    98101
    98104
    98115
    98201
    98203
    98004
    98007
    98065
  • MapReduce (divide and concur)
    HDFS
    • Distributed data storage
    • Distributed processing
    • Language agnostic
    Any Language
    Job tracker
    Task tracker
  • AWS
  • Aggregate Ad Serving data
    Log Files
    File Export
    APIs
    Internet
    Client Provided Data
    Data Sources
    Presentation Layer
    Talend Data Flow Manager
    Direct Analytics Processing via EMR
    Web Application Layer
    ODBC
    Edge Provisioning DB
    OLAP
    Cache
    Cloud Storage S3
    HBase/SDB
    15
    Elastic MapReduce
  • Name Brand Retailer Case Study
    Business challenge
    • Changing competitive landscape
    • Decreasing web marketing effectiveness
    • Monetization of their web assets
  • Bring it all together
    Product interest
    Affinity
    Generation
    +
    +
    In market Gamer
    Sport Enthusiast
    Purchaser Home Theater
    ( 1 of 36 “Personalization” segments )
  • Drive a personalized message
    User recently purchased a home theater system and is now looking for sports games
    Target Ad
    ( 1.7 million per day )
  • We import Atlas transaction level data
    24 servers
    S3 file storage
    Compress and upload 200 + GB of data per day
    ( 180 days = ½ Trillion ICA records )
  • We use EMR to process and segment
    EMR
    S3
    100 Machinecluster created on demand
    ( 3.5 Billion records, 71 million unique cookies a day)
  • Process and Cost
    This all happens in about 8 hours every day and is fully automated (previously 2+ days)
    And increased ROAS by 500% (to $74)
  • Why AWS
    Efficient
    Elastic infrastructure from AWS allows capacity to be provisioned as needed based on load, reducing cost and the risk of processing delays
    Ease of integration
    Amazon Elastic MapReduce with Cascading allows data processing in the cloud without any changes to the underlying algorithms
    Flexible
    Hadoop with Cascading is flexible enough to allow “agile” implementation and unit testing of sophisticated algorithms.
    Adaptable
    Cascading simplifies the integration of Hadoop with external ad system
    Scalable
    AWS infrastructure helps reliably store and process huge (Petabytes) data setss
  • Learning
  • Thank you.