Startup Slide meetup in Mountain View, at Outright.com on 2012-10-09 http://www.meetup.com/startupslide/events/85598842/ Please email if you need a PDF version at pnathan AT concurrentinc DOT com

1. Intro to Data Science
with Cascading
Paco Nathan Document
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@pacoid
Copyright @2012, Concurrent, Inc.

2. opportunity
Unstructured Data
meets
Enterprise Scale
1. backstory: how we got here
2. build: data science teams
3. overview: typical use cases
4. example: Cascading apps

3. Intro to Data Science
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1. backstory:
how we got here

4. inflection point
huge Internet successes after 1997 holiday season…
AMZN, EBAY, Inktomi (YHOO Search), then GOOG 1997
consider this metric: 1998
annual revenue per customer / amount of data stored
which dropped 100x within a few years after 1997
storage and processing costs plummeted, now we must
work much smarter to extract ROI from Big Data… 2004
our methods must adapt
“conventional wisdom” of RDBMS and BI tools became
less viable; business cadre still focused on pivot tables
and pie charts… tends toward inertia!
MapReduce and the Hadoop open source stack grew
directly out of that contention… however, that effort ＋
only solves parts of the puzzle

5. inflection point: consequences
Geoffrey Moore (Mohr Davidow Ventures, author of Crossing The Chasm)
Hadoop Summit, 2012:
“All of Fortune 500 is now on notice over the next 10-year period.”
Amazon and Google as exemplars of massive disruption in retail,
advertising, etc.
data as the major force displacing Global 1000 over the next decade,
mostly through apps — verticals, leveraging domain expertise
Michael Stonebraker (INGRES, PostgreSQL,Vertica,VoltDB, etc.)
XLDB, 2012:
“Complex analytics workloads are now displacing SQL as the basis
for Enterprise apps.”

6. primary sources
Amazon
“Early Amazon: Splitting the website” – Greg Linden
glinden.blogspot.com/2006/02/early-amazon-splitting-website.html
eBay
“The eBay Architecture” – Randy Shoup, Dan Pritchett
addsimplicity.com/adding_simplicity_an_engi/2006/11/you_scaled_your.html
addsimplicity.com.nyud.net:8080/downloads/eBaySDForum2006-11-29.pdf
Inktomi (YHOO Search)
“Inktomi’s Wild Ride” – Erik Brewer (0:05:31 ff)
youtube.com/watch?v=E91oEn1bnXM
Google
“The Birth of Google” – John Battelle
wired.com/wired/archive/13.08/battelle.html
“Underneath the Covers at Google” – Jeff Dean (0:06:54 ff)
youtube.com/watch?v=qsan-GQaeyk
perspectives.mvdirona.com/2008/06/11/JeffDeanOnGoogleInfrastructure.aspx

7. the world before…
BI, SQL, and highly
optimized code

8. data innovation: circa 1996
Stakeholder Customers
Excel pivot tables
PowerPoint slide decks strategy
BI
Product
Analysts
requirements
SQL Query optimized
Engineering code Web App
result sets
transactions
RDBMS

9. the world after…
machine learning,
leveraging log files

10. data innovation: circa 2001
Stakeholder Product Customers
dashboards UX
Engineering
models servlets
recommenders
Algorithmic
+ Web Apps
Modeling classifiers
Middleware
aggregation
event
SQL Query history
result sets customer
transactions
Logs
DW ETL RDBMS

11. the world ahead…
what our customers
are doing now

12. data innovation: circa 2013
Customers
Data Apps
business
Domain process Workflow Prod
Expert
dashboard Web Apps,
metrics
History services Mobile,
data etc. s/w
science dev
Data
Planner
Scientist
social
discovery optimized interactions
+ capacity transactions, Eng
endpoints
modeling content
App Dev
Data Access Patterns
Hadoop, Log In-Memory
etc. Events Data Grid
Ops DW Ops
batch "real time"
Cluster Scheduler
introduced existing
capability SDLC
RDBMS
RDBMS

13. a key difference…

14. statistical thinking
Process Variation Data Tools
employing a mode of thought which includes both logical and analytical reasoning:
evaluating the whole of a problem, as well as its component parts; attempting
to assess the effects of changing one or more variables
this approach attempts to understand not just problems and solutions,
but also the processes involved and their variances
particularly valuable in Big Data work when combined with hands-on experience in
physics – roughly 50% of my peers come from physics or physical engineering…
programmers typically don’t think this way…
however, both systems engineers and data scientists must!

15. most valuable skills
approximately 80% of the costs for data-related projects
get spent on data preparation – mostly on cleaning up
data quality issues: ETL, log file analysis, etc.
unfortunately, data-related budgets for many companies tend
to go into frameworks which can only be used after clean up
most valuable skills:
‣ learn to use programmable tools that prepare data
‣ learn to generate compelling data visualizations
‣ learn to estimate the confidence for reported results
‣ learn to automate work, making analysis repeatable
D3
the rest of the skills – modeling,
algorithms, etc. – those are secondary

16. social caveats
“This data cannot be correct!” may be an early warning
about the organization itself
much depends on how the people whom you work alongside
tend to arrive at decisions:
‣ probably good: Induction, Abduction, Circumscription
‣ probably poor: Deduction, Speculation, Justification
in general, one good data visualization
puts many ongoing verbal arguments to rest
however, let domain experts handle
“data storytelling”, not data scientists
xkcd

17. references
by Leo Breiman
Statistical Modeling:
The Two Cultures
Statistical Science, 2001
bit.ly/eUTh9L

18. references
by Jack Olson
Data Quality
Morgan Kaufmann, 2003
amazon.com/dp/1558608915

19. references
also check out RStudio:
rstudio.org/
rpubs.com/

20. Intro to Data Science
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2. build:
data science teams

21. core values
Data Science teams develop actionable insights,
building confidence for decisions
that work may influence a few decisions worth
billions (e.g., M&A) or billions of small decisions
(e.g., AdWords)
probably somewhere in-between…
solving for pattern, at scale.
an interdisciplinary pursuit which
requires teams, not sole players

22. team process
help people ask the
discovery right questions
allow automation to
modeling place informed bets
deliver products at
integration scale to customers
build smarts into
apps product features Gephi
keep infrastructure
systems running, cost-effective

23. building teams
nn
o
overy
very elliing
e ng ratiio
rat o apps
apps stem
stem
s
s
diisc
d sc mod
mod nteg
iinte
g sy
sy
stakeholder
scientist
developer
ops

24. references
by DJ Patil
Data Jujitsu
O’Reilly, 2012
amazon.com/dp/B008HMN5BE
Building Data Science Teams
O’Reilly, 2011
amazon.com/dp/B005O4U3ZE

25. Intro to Data Science
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3. overview:
typical use cases

26. using science in data science
edoMpUsserD:IUN
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tcudorP evomeR lenaP yrotnevnI tneilC
in a nutshell, what we do…
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edoMstiderCyuB:IUN
tohspanS a ekaT
egapemoH nwO tisiV
elbbuB a epyT
taeS egnahC
wodniW D3 nepO
dneirF ddA
revO tcudorP pilF lenaP yrotnevnI tneilC
lenaP tidE
woN tahC
‣ estimate probability
teP yalP
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2 petS egaP traC esahcruP edaM remotsuC
M215 :gniniamer ecaps sserddA
gnihtolC no tuP
bew :metI na yuB
edoMeivoM:IUN
ytinummoc ,tneilc :detratS weiV eivoM
teP weN etaerC
detrats etius tset :tseTytivitcennoC
emag pazyeh dehcnuaL
eciov mooRcilbuP tahC
egasseM yadhtriB
edoMlairotuT:IUN
ybbol semag dehcnuaL
noitartsigeR euqinU
edoMpUsserD:IUN
tcudorP ylppA lenaP yrotnevnI tneilC
tcudorP evomeR lenaP yrotnevnI tneilC
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edoMmooRcilbuP:IUN
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edoMtahC:IUN
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edoMstiderCyuB:IUN
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t a eS e g n a h C
dneirF ddA
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l e n aP t i dE
woN tahC
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gnihtolC no tuP
bew :metI na yuB
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ytinummoc ,tneilc :detratS weiV eivoM
teP weN etaerC
detrats etius tset :tseTytivitcennoC
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eciov mooRcilbuP tahC
egasseM yadhtriB
edoMlairotuT:IUN
ybbol semag dehcnuaL
noitartsigeR euqinU
wodniW D3 nepO
‣ calculate analytic variance
‣ manipulate order complexity
‣ make use of learning theory
‣ collab with DevOps, Stakeholders

27. probability estimation
“a random variable or stochastic variable is a
variable whose value is subject to variations”
“an estimator is a rule for calculating an
estimate of a given quantity based on observed
data”
estimators and probability
distributions provide the essential
basis for our insights
bayesian methods, shrinkage…
these are our friends
quantile estimation, empirical CDFs…
…versus frequentist notions

28. analytic variance
our tools for automation leverage deep
understanding of covariance
cannot overstate the importance of
sampling… insist on metrics described
as confidence intervals, where valid
bootstrapping, bagging…
these are our friends
Monte Carlo methods resolve “black box”
problems
point estimates may help prevent
“uninformed” decisions
do not skimp on this part, ever…
a hard lesson learned from BI failures

29. order complexity
techniques for manipulating order complexity:
dimensional reduction… with clustering
as a common case
e.g., you may have 100 million HTML docs,
but there are only ~10K useful keywords
low-dimensional structures, PCA
linear algebra tricks: eigenvalues, matrix
decomposition, etc.
many hard problems resolved by “divide and
conquer”
this is an area ripe for much advancement in
algorithms research near-term

30. learning theory
in general, apps alternate between learning
patterns/rules and retrieving similar things…
statistical learning theory – rigorous,
prevents you from making billion dollar
mistakes, probably our future
machine learning – scalable, enables
you to make billion dollar mistakes, much
commercial emphasis
supervised vs. unsupervised
arguably, optimization is a related area
once Big Data projects get beyond merely
digesting log files, optimization will likely
become yet another buzzword :)

31. use case: marketing funnel
• must optimize a very large ad spend
• different vendors report different metrics
Wikipedia
• seasonal variation distorts performance
• some campaigns are much smaller than others
• hard to predict ROI for incremental spend
approach:
• log aggregation, followed with cohort analysis
• bayesian point estimates compare different-sized ad tests
• customer lifetime value quantifies ROI of new leads
• time series analysis normalizes for seasonal variation
• geolocation adjusts for regional cost/benefit
• linear programming models estimate elasticity of demand

32. use case: ecommerce fraud
• sparse data means lots of missing values
stat.berkeley.edu
• “needle in a haystack” lack of training cases
• answers are available in large-scale batch, results
are needed in real-time event processing
• not just one pattern to detect – many, ever-changing
approach:
• random forest (RF) classifiers predict likely fraud
• subsampled data to re-balance training sets
• impute missing values based on density functions
• train on massive log files, run on in-memory grid
• adjust metrics to minimize customer support costs
• detect novelty – report anomalies via notifications

33. use case: customer segmentation
• many millions of customers, hard to determine
which features resonate
Mathworks
• multi-modal distributions get obscured by the
practice of calculating an “average”
• not much is known about individual customers
approach:
• connected components for sessionization, determining
uniques from logs
• estimates for age, gender, income, geo, etc.
• clustering algorithms to group into market segments
• social graph infers “unknown” relationships
• covariance/heat maps visualizes segments vs. feature sets

34. use case: monetizing content
• need to suggest relevant content which would
Digital Humanities
otherwise get buried in the back catalog
• big disconnect between inventory and limited
performance ad market
• enormous amounts of text, hard to categorize
approach:
• text analytics glean key phrases from documents
• hierarchical clustering of char frequencies detects lang
• latent dirichlet allocation (LDA) reduces dimension to
topic models
• recommenders suggest similar topics to customers
• collaborative filters connect known users with less known

35. data+code “political spectrum”
“Notes from the Mystery Machine Bus”
by Steve Yegge, Google
goo.gl/SeRZa
“conservative” “liberal”
(mostly) Enterprise (mostly) Start-Up
risk management customer experiments
assurance flexibility
well-defined schema schema follows code
explicit configuration convention
type-checking compiler interpreted scripts
wants no surprises wants no impediments
Java, Scala, Clojure, etc. PHP, Ruby, Python, etc.
Cascading, Scalding, Cascalog, etc. Hive, Pig, Hadoop Streaming, etc.

36. a selection of great tools…
reporting:
visualization:
Graphite, PowerPivot,
ggplot2, D3, Gephi
analytics/modeling: Pentaho, Jaspersoft, SAS
R, Weka, Matlab, PMML, GLPK
text:
LDA, WordNet, OpenNLP, Mallet, Bixo, NLTK
apps:
Cascading, Scalding, Cascalog, R markdown, SWF
scale-out:
Scalr, RightScale, CycleComputing, vFabric, Beanstalk
graph: column:
Gremlin, Vertica,
GraphLab, HBase, key/val: index: relational:
Neo4J Drill, Redis, Lucene/Solr, usual suspects
Dynamo Membase, ElasticSearch
MySQL
imdg:
Spark, Storm, hadoop:
EMR, HW, MapR, machine data:
Gigaspaces
EMC, Azure, Compute Splunk, collectd, durable storage:
Nagios S3, ASV, GCS,
Riak, Couch

37. Intro to Data Science
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4. example:
Cascading apps

38. the workflow abstraction
cascading.org/category/impatient/
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39. layers of a workflow
business domain expertise, business trade-offs,
process market position, operating parameters, etc.
API Scala, Clojure, Python, Ruby, Java, etc.
language
…envision whatever runs in a JVM
optimize /
schedule major changes in technology now
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compute Apache Hadoop, in-memory local mode
“assembler”
code
substrate
…envision GPUs, streaming, etc.
machine
data Splunk, Nagios, Collectd, New Relic, etc.

40. audience?
• Business Stakeholder POV:
business process management for workflow orchestration (think BPM/BPEL)
• Systems Integrator POV:
system integration of heterogenous data sources and compute platforms
• Data Scientist POV:
a directed, acyclic graph (DAG) on which we can apply Amdahl's Law
• Data Architect POV:
a physical plan for large-scale data flow management
• Software Architect POV:
a pattern language, similar to plumbing or circuit design
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a JAR file, has passed CI, available in a Maven repo

41. 1: copy
public class
Main
{
public static void
main( String[] args )
{
String inPath = args[ 0 ];
String outPath = args[ 1 ];
Source
Properties props = new Properties();
AppProps.setApplicationJarClass( props, Main.class );
HadoopFlowConnector flowConnector = new HadoopFlowConnector( props );
// create the source tap
Tap inTap = new Hfs( new TextDelimited( true, "t" ), inPath );
M // create the sink tap
Tap outTap = new Hfs( new TextDelimited( true, "t" ), outPath );
Sink
// specify a pipe to connect the taps
Pipe copyPipe = new Pipe( "copy" );
// connect the taps, pipes, etc., into a flow
FlowDef flowDef = FlowDef.flowDef().setName( "copy" )
.addSource( copyPipe, inTap )
.addTailSink( copyPipe, outTap );
// run the flow
flowConnector.connect( flowDef ).complete();
1 mapper }
}
0 reducers
10 lines code

42. wait!
ten lines of code
for a file copy…
seems like a lot.

43. same JAR, any scale…
MegaCorp Enterprise IT:
Pb’s data
1000+ node private cluster
EVP calls you when app fails
runtime: days+
Production Cluster:
Tb’s data
EMR w/ 50 HPC Instances
Ops monitors results
runtime: hours – days
Staging Cluster:
Gb’s data
EMR + 4 Spot Instances
CI shows red or green lights
runtime: minutes – hours
Your Laptop:
Mb’s data
Hadoop standalone mode
passes unit tests, or not
runtime: seconds – minutes

44. 2: word count
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18 lines code

45. 3: wc + scrub
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22+10 lines code

46. 4: wc + scrub + stop words
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28+10 lines code

47. 5: tf-idf
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11 mappers
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65+10 lines code

48. 6: tf-idf + tdd
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12 mappers
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76+14 lines code

49. City of Palo Alto open data
Regex Regex
tree
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filter parser species
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CoPA
GIS exprot Tree
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tree
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road
Regex Regex
tsv
parser tsv filter Tree Filter GroupBy Checkpoint
parser CoGroup
Distance tree_dist tree_name shade
M
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Left Albedo Segments Geohash CoGroup
Road
Metadata GPS
Failure RHS M logs
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road
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park
filter reco
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park
github.com/Cascading/CoPA/wiki
‣ GIS export for parks, roads, trees (unstructured / open data)
‣ log files of personalized/frequented locations in Palo Alto via iPhone GPS tracks
‣ curated metadata, used to enrich the dataset
‣ could extend via mash-up with many available public data APIs
Enterprise-scale app: road albedo + tree species metadata + geospatial indexing
“Find a shady spot on a summer day to walk near downtown and take a call…”

50. CoPA: log events

51. CoPA: results 0.12
Estimated Tree Height (meters)
0.10
0.08
count
0
density
100
0.06 200
300
0.04
0.02
0.00
0 10 20 30 40 50
avg_height
‣ addr: 115 HAWTHORNE AVE
‣ lat/lng: 37.446, -122.168
‣ geohash: 9q9jh0
‣ tree: 413 site 2
‣ species: Liquidambar styraciflua
‣ avg height 23 m
‣ road albedo: 0.12
‣ distance: 10 m
‣ a short walk from my train stop ✔

52. drill-down
blog, code/wiki/gists, jars, list, DevOps products:
cascading.org/
github.org/Cascading/
conjars.org/
goo.gl/KQtUL
concurrentinc.com/