AWS Customer Presentation - Razorfish

Edge in the cloud
Salim Hemdani
VP, Experiences and Platforms
@shemdani

500,000,000,000

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

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.

AWS Customer Presentation - Razorfish

by Amazon Web Services

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