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Architecting for Data Science
johann@ifwe.co@jssmith github.com/ifwe
Johann Schleier-Smith
CTO, if(we)
O’Reilly Software Architecture Conference

Boston, March 19, 2015
Data Science
value from data
Alternative Definitions
extraction of knowledge from data
making discoveries in the world of big data
statistics + machine learning + scalable
computation + visualization + computer science +
business acumen + skilled communication
Related and Alternative Language
business intelligence
statistics
data mining
forecasting
business reporting
predictive modeling
analyticsknowledge extraction
value
Types of Value
understanding
revenue
product improvements
projections
new inspirations
predictions
customer satisfaction
Today’s Examples
• >10 million candidates to draw from
• >1000 updates/sec
• Must be responsive to current activity
• Users expect instant query results
Recommendation engine

for dating product
• Real-time is challenging
• Human behavior is
complicated, especially in
social context
• Previous interactions are
perhaps our best hope for
predicting future interactions
• Human connections
• User engagement ecosystem
• Subscription and other revenues
Value
♥
Kaggle competition

with Best Buy data
https://www.kaggle.com/c/acm-sf-chapter-hackathon-small
Kaggle competition

with Best Buy data
“outgoing and social (heavy messaging ---
especially distant recipients and opposite gender,
many outgoing comments, many friend requests to
distant people), doesn’t play Pets much”
“receives many messages, active user,
views many profiles, doesn't use meet me,
sends many messages to distant people”
Heavy user overall, (pets, meet me, messaging)!
“heavy user overall, (pets, meet me, messaging)”
value
data
• Vote history
• Social interaction history
• Profile information
Dating product data
{
“timestamp” : “2011-10-31 09:48:46”,
“query” : “Assassin’s Creed”,
“skuSelected” : “2670133”
}
product views
{
“sku” : “1032361”,
“regularPrice” : “19.99”,
“name” : “Need for Speed: Hot Pursuit”,
“description” : “Fasten your seatbelt and

get ready to drive like your life depends

on it...”
...
}
product updates
Formats for Data
log files
web services
relational databases
unstructured documents
spreadsheets
xml files
Types of Data
technical data
government data
usage records
sensor data
academic data
reference data
yet uncollected data
Vasant Dhar. 2013. Data science and prediction. Commun. ACM 56, 12 (December 2013), 64-73.

And International Telecommunication Union (ITU) and United Nations Population Division via www.internetlivestats.com/internet-users/
Trends
data quantity
machine learning maturing
data variety
data velocity
Machine Learning
classification
decision trees
supervised methods
unsupervised methodsclustering
what matters most is mapping of data
Machine Learning Techniques
• Classification - (Logistic Regression, Decision
Trees, Random Forests)
• Prediction - (Generalized Linear Models, Support
Vector Regression, …)
• Clustering - (K-Means, Hierarchical, Latent
Dirichlet Allocation, …)
• Collaborative filtering, …
Features often matter more than choice of algorithm
data
tools of
the
trade
• Created in 1993
• Implementation of S language but also
inherits from Scheme
• Object oriented code is possible but
not encouraged
• Vast high-quality package ecosystem
• Data is vectors and data frames
Demo
• Statistics
• Visualization
• Machine learning
• REPL, scripts, interactive IDE
• In-memory data sets
http://scikit-learn.org/stable/auto_examples/linear_model/plot_iris_logistic.html
• More of a general purpose language than R
• Arrays and matrices as basic data structures
• Supports data frames through Pandas
• Sophisticated machine learning libraries
• Generally limited to in-memory data sets
• Leverages commodity hardware to
store large data sets at low cost
• Vibrant and diverse ecosystem
• Popular but not always best solution
• Probably best viewed as marketing
terminology, as opposed to technology
https://hadoopecosystemtable.github.io/
Category
Number of
projects
Distributed Filesystem 7
Distributed Programming 18
NoSQL Database 4
Document Data Model 3
Stream Data Model 1
Key-Value Data Model 4
Graph Data Model 3
NewSQL 9
SQL-On-Hadoop 11
Data Ingestion 11
Service Programming 7
Scheduling 3
Machine Learning 6
Benchmarking 5
Security 3
System Deployment 12
Applications 5
Development Frameworks 2
Categorize Pending 16
130 freely licensed open source
projects listed in the Hadoop
Ecosystem Table
Hadoop for Data Scientists
• Pulling data from repository (SQL, Hive)
• MapReduce programming (Java, Scala,
Pig, Python)
• Spark in-memory framework is gaining
adoption rapidly
tools rarely used in data science
version control
automated testing
automated deployment
shared code
agile methodology
code review
software architecture
the cycle

of data science
Data ProcessingData Collection
Models, Algorithms
Data-driven Product
Features
Data Analysis &
Understanding
Reports &
Visualizations
Product Improvements
data science

at
• Profitable startup actively pursuing big
opportunities in social apps
• Millions of users on existing products
• Thousands of social contacts per second
what it should look like
1. Gain understanding of the product usage
2. See opportunity to make the product better
3. Create training data
4. Train predictive models
5. Put models in production
6. See improvements
what it often looks like
1. Gain understanding of the product usage
2. See opportunity to make the product better
3. Pull records from relational database to create
interesting features (usually aggregates)
4. Train predictive models
5. Go implement models for production
6. See improvements
1. Gain understanding of the product usage
2. See opportunity to make the product better
3. Pull records from relational database to create
interesting features (usually aggregates)
4. Train predictive models
5. Go implement models for production
6. See improvements
3-6

months
1. Gain understanding of the product usage
2. See opportunity to make the product better
3. Pull records from relational database to create
interesting features (usually aggregates)
4. Train predictive models
5. Go implement models for production
6. See improvements Cool!

Was it worth it?
implementation

pain points
• Data scientist hands model description to
software engineer
• May need to translate features from SQL to Java
• Aggregate features require batch processing
• May need to adjust features and model to achieve
real-time updates
• Fast scoring requires high-performance in-
memory data structures
new thinking
new architecture
one right way to data
event history
one right way to data
everything is an event
Bob registers
Alice registers
Alice updates profile
Bob opens app
Bob sees Alice in recommendations
Bob swipes yes on Alice
Alice receives push notification
Alice sees Bob swiped yes
Alice swipes yes
Alice sends message to Bob
architecture
comparison
Database'
Applica-on'
Web'API'
Ranking' Solr'Search'
Indexing'
Service'
Change'logs'
Occasional'
index'rebuilds'
Change'logs'
Produc'on)
Development)
Exploratory'
Analysis'
Training'&'
Backtes-ng'
Database'
Applica-on'
Web'API'
Ranking' Solr'Search'
Indexing'
Service'
Change'logs'
Occasional'
index'rebuilds'
Change'logs'
Produc'on)
Development)
Exploratory'
Analysis'
Training'&'
Backtes-ng'
Database'
Applica-on'
Web'API'
Ranking' Solr'Search'
Indexing'
Service'
Change'logs'
Occasional'
index'rebuilds'
Change'logs'
Produc'on)
Development)
Exploratory'
Analysis'
Training'&'
Backtes-ng'
Database'
Applica-on'
Web'API'
Ranking' Solr'Search'
Indexing'
Service'
Change'logs'
Occasional'
index'rebuilds'
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Event'History'Repository'
Solr'Search'
Real=-me'
State'Updates'
Change'logs'Real=-me'events'
Occasional'
index'rebuilds'
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Analysis'&'
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State'Updates'
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Change'logs'Real=-me'events'
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index'rebuilds'
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Training'&'
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State'Updates'
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Event'History'Repository'
Solr'Search'
Real=-me'
State'Updates'
Change'logs'Real=-me'events'
Occasional'
index'rebuilds'
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Analysis'&'
Visualiza-on'
Produc'on)
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Training'&'
Backtes-ng'
State'Updates'
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Database'
Applica-on'
Web'API'
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Event'History'Repository'
Solr'Search'
Real=-me'
State'Updates'
Change'logs'Real=-me'events'
Occasional'
index'rebuilds'
Exploratory'
Analysis'&'
Visualiza-on'
Produc'on)
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Training'&'
Backtes-ng'
State'Updates'
Monitoring'
Database'
Applica-on'
Web'API'
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Solr'Search'
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Change'logs'Real=-me'events'
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index'rebuilds'
Exploratory'
Analysis'&'
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Training'&'
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State'Updates'
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Event History API
trait EventHistory {
def publishEvent(e: Event)
"
def getEvents(
startTime: Date,
endTime: Date,
eventFilter: EventFilter,
eventHandler: EventHandler
)
}
Event History API
trait EventHistory {
def publishEvent(e: Event)
"
def getEvents(
startTime: Date,
endTime: Date,
eventFilter: EventFilter,
eventHandler: EventHandler
)
}
Event History API
trait EventHistory {
def publishEvent(e: Event)
"
def getEvents(
startTime: Date,
endTime: Date,
eventFilter: EventFilter,
eventHandler: EventHandler
)
}
+∞ for

real-time

streaming
training data
comparison
Database'
Applica-on'
Web'API'
Ranking' Solr'Search'
Indexing'
Service'
Change'logs'
Occasional'
index'rebuilds'
Change'logs'
Produc'on)
Development)
Exploratory'
Analysis'
Training'&'
Backtes-ng'
Events'State'Snapshots''
Training'
Features'
Training'
Outcomes'
Time'
Database'
Applica-on'
Web'API'
Ranking'
Event'History'Repository'
Solr'Search'
Real=-me'
State'Updates'
Change'logs'Real=-me'events'
Occasional'
index'rebuilds'
Exploratory'
Analysis'&'
Visualiza-on'
Produc'on)
Development)
Training'&'
Backtes-ng'
State'Updates'
Monitoring'
Updates(
Training(
features(
Training(
outcome(
Time(
Online(State(
Events(
Training(Data(
1. Gain understanding of the product usage
2. See opportunity to make the product better
3. Create training data
4. Train predictive models
5. Put models in production
6. See improvements
Fast cycles!!
Live Demo
https://www.kaggle.com/c/acm-sf-chapter-hackathon-small
https://github.com/ifwe/antelope
• Open source implementation derived from if(we)’s
proprietary platform
• Not ready scale or production, but useful for
demonstration purposes
• Seeking collaborators
product update events
{
“timestamp” : “2012-05-03 6:43:15”,
“eventType” : “ProductUpdate”,
“eventProperties” : {
“sku” : “1032361”,
“regularPrice” : “19.99”,
“name” : “Need for Speed: Hot Pursuit”,
“description” : “Fasten your seatbelt and

get ready to drive like your life depends

on it...”
...
}
}
product view events
{
“timestamp” : “2011-10-31 09:48:46”,
“eventType” : “ProductView”,
“eventProperties” : {
“query” : “Modern warfare”,
“skuSelected” : “2670133”
}
}
demo
Try it yourself, code and instructions at:

https://github.com/ifweco/antelope/blob/master/doc/demo.md
new
tools :)
Architecture for

Data Science
data warehousing
Database'
Applica-on'
Web'API'
Ranking' Solr'Search'
Indexing'
Service'
Change'logs'
Occasional'
index'rebuilds'
Change'logs'
Produc'on)
Development)
Exploratory'
Analysis'
Training'&'
Backtes-ng'
Data$
Warehouse$
Extract$
Load$
Transform$
Opera7onal$
Data$Store$
Staging$
log transform use
extract transform load
Data Architecture
dimensional modeling
relational modeling
Warehouse Design
normalization
slowly changing

dimensions
denormalization
star schema
slowly changing dimensions
type 1: overwrite the old data
type 2: multiple rows with versioning
type 3: extra columns for older versions
slowly changing dimensions
event history
Database'
Applica-on'
Web'API'
Ranking'
Event'History'Repository'
Solr'Search'
Real=-me'
State'Updates'
Change'logs'Real=-me'events'
Occasional'
index'rebuilds'
Exploratory'
Analysis'&'
Visualiza-on'
Produc'on)
Development)
Training'&'
Backtes-ng'
State'Updates'
Monitoring'
Event&
Indexing&
Applica1on&
Web&API&
Ranking&
Event&History&Repository&
Solr&Search&
Real>1me&
State&Updates&
Change&logs&Real>1me&events&
Exploratory&
Analysis&&&
Visualiza1on&
Produc'on)
Development)
Training&&&
Backtes1ng&
State&Updates&
Monitoring&
trait EventHistory {
def publishEvent(e: Event)
"
def getEvents(
startTime: Date,
endTime: Date,
eventFilter: EventFilter,
eventHandler: EventHandler
)
}
event history design
Data Architecture
ok to denormalize
log a lot
think about the types
• Make sure that events are simple facts
• Files are ok for event history, don’t really need a database
• Use an object hierarchy to model events in code
• Use online features that are efficient to update incrementally
• Write efficient implementations before than scaling out
• Functional style makes it easier
• Encourage reactive processing
Data Quality
• Matters more than transformations, more than algorithms
• Data that doesn’t make sense often indicates an application bug
• Do assertions, e.g., make sure things aren’t happening out of order
• All data in form of events – no exceptions!
• Same feature code in production and development
• Emphasis on creative feature engineering
• Quick cycles between ideas and production
github.com/ifwe/antelope
@jssmith
Try the Antelope Demo:

https://github.com/ifwe/antelope/blob/master/doc/demo.md

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