Intro to Data Science on Hadoop

@joe_Caserta#DataSummit
Introduction to Data Science
On Hadoop
Joe Caserta
President
Caserta Concepts

Caserta Timeline
Launched Big Data practice Co-author, with Ralph Kimball, The Data
Warehouse ETL Toolkit (Wiley)
Data Analysis, Data Warehousing and Business
Intelligence since 1996
Began consulting database programing and data
modeling 25+ years hands-on experience building database
solutions
Founded Caserta Concepts in NYC
Web log analytics solution published in Intelligent
Enterprise
Launched Data Science, Data Interaction and
Cloud practices Laser focus on extending Data Analytics with Big
Data solutions
1986
2004
1996
2009
2001
2013
2012
2014
Dedicated to Data Governance Techniques on Big
Data (Innovation)
Awarded Top 20 Big Data
Companies 2016
Top 20 Most Powerful
Big Data consulting firms
Launched Big Data Warehousing (BDW) Meetup
NYC: 2,000+ Members
2016 Awarded Fastest Growing Big
Data Companies 2016
Established best practices for big data ecosystem
implementations

About Caserta Concepts
• Technology services company with expertise in data analysis:
• Big Data Solutions
• Data Warehousing
• Business Intelligence
• Core focus in the following industries:
• eCommerce / Retail / Marketing
• Financial Services / Insurance
• Healthcare / Ad Tech / Higher Ed
• Established in 2001:
• Increased growth year-over-year
• Industry recognized work force
• Strategy and Implementation
• Data Science & Analytics
• Data on the Cloud
• Data Interaction & Visualization

Agenda
• Why we care about Big Data
• Challenges of working with Big Data
• Governing Big Data for Data Science
• Introducing the Data Pyramid
• Why Data Science is Cool?
• What does a Data Scientist do?
• Standards for Data Science
• Business Objective
• Data Discovery
• Preparation
• Models
• Evaluation
• Deployment
• Q & A

Enrollments
Claims
Finance
ETL
Ad-Hoc Query
Horizontally Scalable Environment - Optimized for Analytics
Big Data Lake
Canned Reporting
Big Data Analytics
NoSQL
DatabasesETL
Ad-Hoc/Canned
Reporting
Traditional BI
Spark MapReduce Pig/Hive
N1 N2 N4N3 N5
Hadoop Distributed File System (HDFS)
Traditional
EDW
Others…
Today’s business environment requires Big Data
Data Science

•Data is coming in so
fast, how do we
monitor it?
•Real real-time
analytics
•What does
“complete” mean
•Dealing with sparse,
incomplete, volatile,
and highly
manufactured data.
How do you certify
sentiment analysis?
•Wider breadth of
datasets and sources
in scope requires
larger data
governance support
•Data governance
cannot start at the
data warehouse
•Data volume is higher
so the process must
be more reliant on
programmatic
administration
•Less people/process
dependence
Volume Variety
VelocityVeracity
The Challenges Building a Data Lake

What’s Old is New Again
 Before Data Warehousing Governance
 Users trying to produce reports from raw source data
 No Data Conformance
 No Master Data Management
 No Data Quality processes
 No Trust: Two analysts were almost guaranteed to come up with two
different sets of numbers!
 Before Data Lake Governance
 We can put “anything” in Hadoop
 We can analyze anything
 We’re scientists, we don’t need IT, we make the rules
 Rule #1: Dumping data into Hadoop with no repeatable process, procedure, or data governance
will create a mess
 Rule #2: Information harvested from an ungoverned systems will take us back to the old days: No
Trust = Not Actionable

Making it Right
 The promise is an “agile” data culture where communities of users are encouraged to explore
new datasets in new ways
 New tools
 External data
 Data blending
 Decentralization
 With all the V’s, data scientists, new tools, new data we must rely LESS on HUMANS
 We need more systemic administration
 We need systems, tools to help with big data governance
 This space is EXTREMELY immature!
 Steps towards Data Governance for the Data Lake
1. Establish difference between traditional data and big data governance
2. Establish basic rules for where new data governance can be applied
3. Establish processes for graduating the products of data science to governance
4. Establish a set of tools to make governing Big Data feasible

Process Architecture
Communication
Organization
IFP
Governance
Administration
Compliance
Reporting
Standards
Value Proposition
Risk/Reward
Information
Accountabilities
Stewardship
Architecture
Enterprise Data
Council
Data Integrity
Metrics
Control Mechanisms
Principles and
Standards
Information Usability
Communication
BDG provides vision, oversight and accountability for leveraging corporate
information assets to create competitive advantage, and accelerate the vision
of integrated delivery.
Value Creation
• Acts on Requirements
Build Capabilities
• Does the Work
• Responsible for adherence
Governance
Committees
Data Stewards
Project Teams
Enterprise Data
Council
• Executive Oversight
• Prioritizes work
Drives change
Accountable for results
Definitions
Data Governance for the Data Lake

•This is the ‘people’ part. Establishing Enterprise Data Council, Data Stewards, etc.Organization
•Definitions, lineage (where does this data come from), business definitions, technical
metadataMetadata
•Identify and control sensitive data, regulatory compliancePrivacy/Security
•Data must be complete and correct. Measure, improve, certifyData Quality and Monitoring
•Policies around data frequency, source availability, etc.Business Process Integration
•Ensure consistent business critical data i.e. Members, Providers, Agents, etc.Master Data Management
•Data retention, purge schedule, storage/archiving
Information Lifecycle
Management (ILM)
Components of Data Governance

•This is the ‘people’ part. Establishing Enterprise Data Council, Data Stewards, etc.Organization
•Definitions, lineage (where does this data come from), business definitions, technical
metadataMetadata
•Identify and control sensitive data, regulatory compliancePrivacy/Security
•Data must be complete and correct. Measure, improve, certifyData Quality and Monitoring
•Policies around data frequency, source availability, etc.Business Process Integration
•Ensure consistent business critical data i.e. Members, Providers, Agents, etc.Master Data Management
•Data retention, purge schedule, storage/archiving
Information Lifecycle
Management (ILM)
Components of Data Governance
• Add Big Data to overall framework and assign responsibility
• Add data scientists to the Stewardship program
• Assign stewards to new data sets (twitter, call center logs, etc.)
• Graph databases are more flexible than relational
• Lower latency service required
• Distributed data quality and matching algorithms
• Data Quality and Monitoring (probably home grown, drools?)
• Quality checks not only SQL: machine learning, Pig and Map Reduce
• Acting on large dataset quality checks may require distribution
• Larger scale
• New datatypes
• Integrate with Hive Metastore, HCatalog, home grown tables
• Secure and mask multiple data types (not just tabular)
• Deletes are more uncommon (unless there is regulatory requirement)
• Take advantage of compression and archiving (like AWS Glacier)
• Data detection and masking on unstructured data upon ingest
• Near-zero latency, DevOps, Core component of business operations
For Big Data

Data Lake Governance Realities
 Full data governance can only be applied to “Structured” data
 The data must have a known and well documented schema
 This can include materialized endpoints such as files or tables OR projections
such as a Hive table
 Governed structured data must have:
 A known schema with Metadata
 A known and certified lineage
 A monitored, quality test, managed process for ingestion and transformation
 A governed usage  Data isn’t just for enterprise BI tools anymore
 We talk about unstructured data in Hadoop but more-so it’s semi-
structured/structured with a definable schema.
 Even in the case of unstructured data, structure must be extracted/applied in
just about every case imaginable before analysis can be performed.

The Data Scientists Can Help!
 Data Science to Big Data Warehouse mapping
 Full Data Governance Requirements
 Provide full process lineage
 Data certification process by data stewards and business owners
 Ongoing Data Quality monitoring that includes Quality Checks
 Provide requirements for Data Lake
 Proper metadata established:
 Catalog
 Data Definitions
 Lineage
 Quality monitoring
 Know and validate data completeness

Big
Data
Warehouse
Data Science Workspace
Data Lake
Landing Area
The Big Data Analytics Pyramid
Metadata  Catalog
ILM  who has access, how long do
we “manage it”
Raw machine data
collection, collect
everything
Data is ready to be turned into
information: organized, well defined,
complete.
Agile business insight through data-munging,
machine learning, blending with external data,
development of to-be BDW facts
ILM  who has access, how long do we “manage it”
Data Quality and Monitoring 
Monitoring of completeness of data
ILM  who has access, how long do we “manage it”
Data Quality and Monitoring  Monitoring of
completeness of data
 Hadoop has different governance demands at each tier.
 Only top tier of the pyramid is fully governed.
 We refer to this as the Trusted tier of the Big Data Warehouse.
Fully Data Governed ( trusted)User community arbitrary queries and reporting
Usage Pattern Data Governance

What does a Data Scientist Do, Anyway?
 Searching for the data they need
 Making sense of the data
 Figuring why the data looks the way is does and assessing its validity
 Cleaning up all the garbage within the data so it represents true business
 Combining events with Reference data to give it context
 Correlating event data with other events
 Finally, they write algorithms to perform mining, clustering and predictive analytics
 Writes really cool and sophisticated
algorithms that impacts the way the business
runs.
 Much of the time of a Data Scientist is spent:
 NOT

Why Data Science?
Descriptive
Analytics
Diagnostic
Analytics
Predictive
Analytics
Prescriptive Analytics
What
happened?
Why did it
happen?
What will
happen?
How can we make
It happen?
Data Analytics Sophistication
BusinessValue
Source: Gartner

The Data Scientist Winning Trifecta
Modern Data
Engineering/Data
Preparation
Domain
Knowledge/Bu
siness
Expertise
Advanced
Mathematics/
Statistics

Easier to Find Than an Awesome Data Scientist

Modern Data Engineering

Which Visualization, When?

Advanced Mathematics / Statistics

Domain and Outcome Sensibility

Are there Standards?
CRISP-DM: Cross Industry Standard Process for Data Mining
1. Business Understanding
• Solve a single business problem
2. Data Understanding
• Discovery
• Data Munging
• Cleansing Requirements
3. Data Preparation
• ETL
4. Modeling
• Evaluate various models
• Iterative experimentation
5. Evaluation
• Does the model achieve business objectives?
6. Deployment
• PMML; application integration; data platform; Excel

1. Business Understanding
In this initial phase of the project we will need to speak to humans.
• It would be premature to jump in to the data, or begin selection of
the appropriate model(s) or algorithm
• Understand the project objective
• Review the business requirements
• The output of this phase will be conversion of business requirements
into a preliminary technical design (decision model) and plan.
Since this is an iterative process, this phase will be revisited throughout
the entire process.

Data
ScientistBusiness
Analyst
Business Stakeholders
Interview notes
Requirement Document
Models / Insights
Gathering Requirements

Data Science Scrum Team
Data
Scientist
Business
Stakeholders
Data
Engineer
Efficient Inclusive
EffectiveInteractive
Data
Analyst

2. Data Understanding
• Data Discovery  understand where the data you need comes
from
• Data Profiling  interrogate the data at the entity level,
understand key entities and fields that are relevant to the
analysis.
• Cleansing Requirements  understand data quality, data
density, skew, etc
• Data Munging  collocate, blend and analyze data for early
insights! Valuable information can be achieved from simple
group-by, aggregate queries, and even more with SQL Jujitsu!
Significant iteration between Business Understanding and Data
Understanding phases.
Sample
Exploration tools
for Hadoop:
Trifacta, Paxata,
Spark, Python,
Pig, Hive,
Waterline,
Elasticsearch

Data Exploration in Hadoop - Avoid low level coding
Start by evaluating DSL’s
Structured/tab
ular
Hive
Pig
Core or
Extended
Libraries
Will a
Custom UDF
help?
Use Streaming or
Native MR
Yes
Yes
No
No
Yes
Practical to
express in
SQL
Yes
No
No
Spark

Data Science Data Quality Priorities
Be
Corrective
Be Fast
Be
Transparent
Be Thorough

Data Science Data Quality Priorities
Data Quality
SpeedtoValue
Fast
Slow
Raw Refined
Does Data munging in a data science
lab need the same restrictive
governance and enterprise reporting?

3. Data Preparation
ETL (Extract Transform Load)
90+% of a Data Scientists time goes into Data Preparation!
• Select required entities/fields
• Address Data Quality issues: missing or incomplete values,
whitespace, bad data-points
• Join/Enrich disparate datasets
• Transform/Aggregate data for intended use:
• Sample
• Aggregate
• Pivot

Data Preparation
• We love Spark!
• ETL can be done in Scala,
Python or SQL
• Cleansing, transformation,
and standardization
• Address Parsing:
usaddress, postal-address,
etc
• Name Hashing: fuzzy, etc
• Genderization:
sexmachine, etc
• And all the goodies of the
standard Python library!
• Parallelize workload
against a large number of
machines in Hadoop
cluster

Data Quality and Monitoring
• BUILD a robust data quality subsystem:
• Metadata and error event facts
• Orchestration
• Based on Data Warehouse ETL Toolkit
• Each error instance of each data quality
check is captured
• Implemented as sub-system after
ingestion
• Each fact stores unique identifier of the
defective source row
HAMBot: ‘open
source’ project
created in Caserta
Innovation Lab
(CIL)

4. Modeling
Do you love algebra & stats?
• Evaluate various models/algorithms
• Classification
• Clustering
• Regression
• Many others…..
• Tune parameters
• Iterative experimentation
• Different models may require different data preparation
techniques (ie. Sparse Vector Format)
• Additionally we may discover the need for additional data points,
or uncover additional data quality issues!

Modeling in Hadoop
• Spark works well
• SAS, SPSS, Etc. not
native on Hadoop
• R and Python
becoming new
standard
• PMML can be used,
but approach with
caution

Machine Learning
The goal of machine learning is to get software to make decisions and learn
from data without being programed explicitly to do so
Machine Learning algorithms are broadly broken out into two groups:
• Supervised learning  inferring functions based on labeled training data
• Unsupervised learning  finding hidden structure/patterns within data, no
training data is supplied
We will review some popular, easy to understand machine learning
algorithms

What to use When?

Supervised Learning
Name Weight Color Cat_or_Dog
Susie 9lbs Orange Cat
Fido 25lbs Brown Dog
Sparkles 6lbs Black Cat
Fido 9lbs Black Dog
Name Weight Color Cat_or_Dog
Misty 5lbs Orange ?
The training set is used to generate a function
..so we can predict if we have a cat or dog!

Category or Values?
There are several classes of algorithms depending on whether the prediction is a
category (like cat or dog) or a value, like the value of a home.
Classification algorithms are generally well fit for categorization, while algorithms
like Regression and Decision Trees are well suited for predicting values.

Regression
• Understanding the relationship between a given set of dependent variables
and independent variables
• Typically regression is used to predict the output of a dependent variable
based on variations in independent variables
• Very popular for prediction and forecasting
Linear Regression

Decision Trees
• A method for predicting outcomes based on the features of data
• Model is represented a easy to understand tree structure of if-else statements
Weight > 10lbs
color = orange
cat
yes
no
name = fido
no
no
dogyes
dog
cat
yes

Unsupervised K-Means
• Treats items as coordinates
• Places a number of random “centroids”
and assigns the nearest items
• Moves the centroids around based on
average location
• Process repeats until the assignments
stop changing
Clustering of items into logical groups based on natural patterns in data
Uses:
• Cluster Analysis
• Classification
• Content Filtering

Collaborative Filtering
• A hybrid of Supervised and Unsupervised Learning (Model Based vs. Memory
Based)
• Leveraging collaboration between multiple agents to filter, project, or detect
patterns
• Popular in recommender systems for projecting the “taste” for of specific
individuals for items they have not yet expressed one.

Item-based
• A popular and simple memory-based collaborative filtering algorithm
• Projects preference based on item similarity (based on ratings):
for every item i that u has no preference for yet
for every item j that u has a preference for
compute a similarity s between i and j
add u's preference for j, weighted by s, to a running average
return the top items, ranked by weighted average
• First a matrix of Item to Item similarity is calculated based on user rating
• Then recommendations are created by producing a weighted sum of top items,
based on the users previously rated items

5. Evaluation
What problem are we trying to solve again?
• Our final solution needs to be evaluated against original
Business Understanding
• Did we meet our objectives?
• Did we address all issues?

6. Deployment
Engineering Time!
• It’s time for the work products of data science to “graduate” from “new
insights” to real applications.
• Processes must be hardened, repeatable, and generally perform well too!
• Data Governance applied
• PMML (Predictive Model Markup Langauge): XML based interchange format
Big$
Data$
Warehouse$
Data$Science$Workspace$
Data$Lake$–$Integrated$Sandbox$$
Landing$Area$–$Source$Data$in$“Full$Fidelity”$
New
Data
New
Insights
Governance
Refinery

My Favorite Data Science Project
• Recommendation Engines

Project Objective
• Create a functional recommendation engine to surface to provide relevant
product recommendations to customers.
• Improve Customer Experience
• Increase Customer Retention
• Increase Customer Purchase Activity
• Accurately suggest relevant products to customers based on their peer
behavior.

Recommendations
• Your customers expect them
• Good recommendations make life easier
• Help them find information, products, and services they might not have
thought of
• What makes a good recommendation?
• Relevant but not obvious
• Sense of “surprise”
23” LED TV 24” LED TV 25” LED TV
23” LED TV``
SOLD!!
Blu-Ray Home Theater HDMI Cables

Where do we use recommendations?
• Applications can be found in a wide variety of industries and applications:
• Travel
• Financial Service
• Music/Online radio
• TV and Video
• Online Publications
• Retail
..and countless others
Our Example: Movies

The Goal of the Recommender
• Create a powerful, scalable recommendation engine with minimal development
• Make recommendations to users as they are browsing movie titles -
instantaneously
• Recommendation must have context to the movie they are currently viewing.
OOPS! – too much surprise!

Recommender Tools & Techniques
Hadoop – distributed file system and processing platform
Spark – low-latency computing
MLlib – Library of Machine Learning Algorithms
We leverage two algorithms:
• Content-Based Filtering – how similar is this particular movie to other movies based on
usage.
• Collaborative Filtering – predict an individuals preference based on their peers ratings.
Spark MLlib implements a collaborative filtering algorithm called Alternating Least Squares
(ALS)
• Both algorithms only require a simple dataset of 3 fields:
“User ID” , “Item ID”, “Rating”

Content-Based Filtering
“People who liked this movie liked these as well”
• Content Based Filter builds a matrix of items to other items and calculates
similarity (based on user rating)
• The most similar item are then output as a list:
• Item ID, Similar Item ID, Similarity Score
• Items with the highest score are most similar
• In this example users who liked “Twelve Monkeys” (7) also like “Fargo” (100)
7 100 0.690951001800917
7 50 0.653299445638532
7 117 0.643701303640083

Collaborative Filtering
“People with similar taste to you liked these movies”
• Collaborative filtering applies weights based on “peer” user preference.
• Essentially it determines the best movie critics for you to follow
• The items with the highest recommendation score are then output as tuples
• User ID [Item ID1:Score,…., Item IDn:Score]
• Items with the highest recommendation score are the most relevant to this user
• For user “Johny Sisklebert” (572), the two most highly recommended movies are “Seven” and
“Donnie Brasco”
572 [11:5.0,293:4.70718,8:4.688335,273:4.687676,427:4.685926,234:4.683155,168:4.669672,89:4.66959,4:4.65515]
573 [487:4.54397,1203:4.5291,616:4.51644,605:4.49344,709:4.3406,502:4.33706,152:4.32263,503:4.20515,432:4.26455,611:4.22019]
574 [1:5.0,902:5.0,546:5.0,13:5.0,534:5.0,533:5.0,531:5.0,1082:5.0,1631:5.0,515:5.0]

Recommendation Store
• Serving recommendations needs to be instantaneous
• The core to this solution is two reference tables:
• When called to make recommendations we query our store
• Rec_Item_Similarity based on the Item_ID they are viewing
• Rec_User_Item_Base based on their User_ID
Rec_Item_Similarity
Item_ID
Similar_Item
Similarity_Score
Rec_User_Item_Base
User_ID
Item_ID
Recommendation_Score

Delivering Recommendations
Item-Based:
Peers like these
Movies
Best
Recommendations
Item Similarity Raw Score Score
Fargo 0.691 1.000
Star Wars 0.653 0.946
Rock, The 0.644 0.932
Pulp Fiction 0.628 0.909
Return of the Jedi 0.627 0.908
Independence Day 0.618 0.894
Willy Wonka 0.603 0.872
Mission: Impossible 0.597 0.864
Silence of the Lambs, The 0.596 0.863
Star Trek: First Contact 0.594 0.859
Raiders of the Lost Ark 0.584 0.845
Terminator, The 0.574 0.831
Blade Runner 0.571 0.826
Usual Suspects, The 0.569 0.823
Seven (Se7en) 0.569 0.823
Item-Base (Peer) Raw Score Score
Seven 5.000 1.000
Donnie Brasco 4.707 0.941
Babe 4.688 0.938
Heat 4.688 0.938
To Kill a Mockingbird 4.686 0.937
Jaws 4.683 0.937
Monty Python, Holy Grail 4.670 0.934
Blade Runner 4.670 0.934
Get Shorty 4.655 0.931
Top 10 Recommendations
So if Johny is viewing “12 Monkeys” we query our recommendation store and present the results
Seven (Se7en) 1.823
Blade Runner 1.760
Fargo 1.000
Star Wars 0.946
Donnie Brasco 0.941
Babe 0.938
Heat 0.938
To Kill a Mockingbird 0.937
Jaws 0.937
Monty Python, Holy Grail 0.934

From Good to Great Recommendations
• Note that the first 5 recommendations look pretty good
…but the 6th result would have been “Babe” the children's movie
• Tuning the algorithms might help: parameter changes, similarity measures.
• How else can we make it better?
1. Delivery filters
2. Introduce additional algorithms such as K-Means
OOPS!

Additional Algorithm – K-Means
We would use the major attributes of the Movie to create coordinate points.
• Categories
• Actors
• Director
• Synopsis Text
“These movies are similar based on their attributes”

Delivery Scoring and Filters
• One or more categories must match
• Only children movies will be recommended for children's movies.
Action Adventure Children's Comedy Crime Drama Film-Noir Horror Romance Sci-Fi Thriller
Twelve Monkeys 0 0 0 0 0 1 0 0 0 1 0
Babe 0 0 1 1 0 1 0 0 0 0 0
Seven (Se7en) 0 0 0 0 1 1 0 0 0 0 1
Star Wars 1 1 0 0 0 0 0 0 1 1 0
Blade Runner 0 0 0 0 0 0 1 0 0 1 0
Fargo 0 0 0 0 1 1 0 0 0 0 1
Willy Wonka 0 1 1 1 0 0 0 0 0 0 0
Monty Python 0 0 0 1 0 0 0 0 0 0 0
Jaws 1 0 0 0 0 0 0 1 0 0 0
Heat 1 0 0 0 1 0 0 0 0 0 1
Donnie Brasco 0 0 0 0 1 1 0 0 0 0 0
To Kill a Mockingbird 0 0 0 0 0 1 0 0 0 0 0
Apply assumptions to control the results of collaborative filtering
Similarly logic could be applied to promote more favorable options
• New Releases
• Retail Case: Items that are on-sale, overstock

Integrating K-Means into the process
Collaborative Filter K-Means:
Similar
Content Filter
Best
Recommendations
Movies recommended by more than 1 algorithm are the most highly rated

61
Sophisticated Recommendation Model
What items are we
promoting at time
of sale?
What items are
being promoted by
the Store or
Market?
What are people
with similar
characteristics
buying?
Peer Based
Item
Clustering
Corporate
Deals/
Offers
Customer
Behavior
Market/
Store
Recommendation
What items have
you bought in the
past?
What did people
who ordered
these items also
order?
The solution
allows balancing
of algorithms to
attain the most
effective
recommendation

Summary
• Hadoop and Spark can provide a relatively low cost and extremely scalable platform
for Data Science
• Hadoop offers great scalability and speed to value without the overhead of
structuring data
• Spark, with MLlib offers a great library of established Machine Learning algorithms,
reducing development efforts
• Python and SQL tools of choice for Data Science on Hadoop
• Go Agile and follow Best Practices (CRISP-DM)
• Employ Data Pyramid concepts to ensure data has just enough governance

Some Thoughts – Enable the Future
 Data Science requires the convergence of data
quality, advanced math, data engineering and
visualization and business smarts
 Make sure your data can be trusted and people can
be held accountable for impact caused by low data
quality.
 Good data scientists are rare: It will take a village
to achieve all the tasks required for effective data
science
 Get good!
 Be great!
 Blaze new trails!
https://exploredatascience.com/
Data Science Training:

Thank You / Q&A
Joe Caserta
President, Caserta Concepts
joe@casertaconcepts.com
@joe_Caserta

Intro to Data Science on Hadoop

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