1. Data
Science
Studio
19 customers
Founded in
January 2013
Data Science
For Everyone

2. (big) data(s)
+ machine learning
+ for practical applications
= Data Science

3. The Project
(c) Dataiku 2013 - Confidential
Hal Alowne
BI Manager
Dim’s Private Showroom
Dim Sum
CEO & Founder
Dim’s Private Showroom
Medium size e-commerce
• 100M$ revenue
• 1 Data Analyst
Big Guys
$10B + revenue
100+ Data Scientists
Hey Hal ! We need a big data
platform, like the big guys!
Let’s just do as they do!

4. Hal Wish #1
Global Customer Value Funnel
SEO
NewsLetter
Display
Retargeting
Display
AdWords Marketplace
Direct Sales
Delivery
View Basket
Support
Returns
$
$
$ $
Orders

5. Hal Wish #2
Why people drop basket ?
9/30/13 5
Basket
Payment refused
Credit Refused
Cheaper elsewhere ?
Delivery costs ?
Wait Xmas?
ACTION

6. Hal Wish #3
What product to put on top ?
9/30/13 6
Original
Most Popular on top
Better
Machine Learning Score
(age/discount/margin…)
Advanced
Machine Learning Score
+ Personalization

7. 9/30/13 7
Why is it
so
complicated
?

8. Partner Data Spaghetti
Mailing
Partner
DMP
Partnerz
Mail
Optimizer
Retargeter
Market
Data
Providers
Social z
Networks

9. Database are Full
9/30/13 9
1 TB
BI Database
20 TB
BI Database
Any new computing job take
> 1 day
NEED FOR SCALE

10. Architecture Bingo
9/30/13 10
BI Real-TimeBatch Real Real-Time
Simple
Queries
Statistics
Machine
Learning
Hive
Pig
Spark
MongoDB
ElasticSearch
Cascading
R

11. Hadoop
Ceph
Sphere
Cassandra
Spark
Scikit-Learn
Mahout
WEKA
MLBase
RapidMiner
Panda
D3
Crossfilter
InfiniDB
LucidDB
Impala
Elastic Search
SOLR
MongoDB
Riak
Membase
Pig
Hive
Cascading
Talend
Machine Learning !
Mystery Land!
Scalability Central!NoSQL-Slavia!
SQL Columnar Republic!
Vizualization County!
Data Cleanup Wasteland!
Statistician Old !
House!
R

12. Hal’s Bingo !
9/30/13 12
HADOOP
Google Cloud Platform
Dataiku

13. Dataiku
Open Source Web Tracker
(WT1)
} Apache License
} Javascript & IO
} Write directly to Google
Cloud Storage
} Full Java, Easy To Deploy
Step 1
Get your own data
9/30/13 13
Silent in night
Autoscale during Sales
summer and winter

14. Step 2
Mix All Your Data
9/30/13 14
4 VMs on GCE
Tracking Data
Internal Data
Partner Data
Data Science Studio
Pig
Hive
HADOOP
auto-sync
to BigQuery

15. Step 3
Mine your Data
9/30/13 15
Builtin Predictive Models
Advanced Adhoc Models
(R or Python)
Shared Web Based
Data Mining
Platform

16. } January
◦ Choose Partner / Setup the architecture
} February
◦ Initial Deployment : 4TB
◦ Replace BI
} May
◦ New Applications (SEO, …)
} September
◦ Scale Deployment to 15TB
◦ Integrate all channels
Typical Project Calendar
9/30/13 16

17. } Enhance Daily Report Availability
◦ Previous architecture
– Between H+17 and H+26 (!)
◦ Hadoop on GCE
– Between H+3 AND H+7
} +21% Email Channel Optimization
} SEO plan optimization
} and a dozen BI Style “apps”
Some Success For the Project
9/30/13 17

18. Thank you !
9/30/13 18
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@dataiku
Ask any big data question
florian.douetteau@dataiku.com