1.
Demystify

2.
Technology Basics
Big Data Overview & Snapshot
Big Data Architecture : Deep Dive
Hadoop Overview
Clear Understanding of Data Science
Big Data Career Opportunities
Q & A
1
What we will cover in the 60 mins
2
3
4
5
6
7

3.
Apart from that we will also cover …
• An overview of the shift to Data Science Platforms
• The 3 critical components of a Data Science platform
• Industries that are most likely to get disrupted and shift to Data Science
• Characteristics of firms that get left behind the Data Science wave
• Factors that push an industry towards Data Science
• A brief overview of aspects of platform architecture beyond technology

4.
Who am I ?
• Mahesh Kumar CV is A Big Data Entrepreneur
• Mahesh got about 14 years of experience in architecting and
developing distributed and real-time data-driven systems.
• Specialties: Translating big data into action, Big Data Trainings,
Product Engineering Services, and Building Big Data CoE & Big Data
Incubators
• Written more than 60 Blogs in Big Data & SAP Analytics
• Worked in the past with IBM, Mindtree, CSC & Rolta companies
• Conducted couple of Boot camps & Workshops in Different
companies

5.
Data Vs Information
• Data refers to a collection of numbers, characters and is a relative term;
• Data is Raw, Facts , Figures etc
• Information is Process Data

6.
Structure Data Vs Unstructured Data

7.
So where is this data getting generated ?
Social Networking
and Media:
700 million
Facebook users, 250
million Twitter users
175+ million
public blogs
Each Facebook
update, Tweet, blog
post and comment
creates multiple
new data points,
both structured,
semi-structured and
unstructured
Mobile Devices:
5 billion mobile
phones in use
worldwide
Each call, text and
instant message
is logged as data
particularly smart
phones and
tablets, also make
it easier to use
social media
Internet
Transactions:
Billions of online
purchases, stock
trades and other
transactions
happen every day,
including countless
automated
transactions
Each creates a
number of data
points collected
by retailers,
banks, credit
cards, credit
agencies and
others
Networked Devices
and Sensors:
Electronic
devices of all
sorts – including
servers and other
IT hardware,
smart energy
meters and
temperature
sensors -- all
create semi-
structured log
data that record
every action

8.
Build Vs Buy
HUMAN DRIVEN
EMAIL
WEB LOGS
DOCUMENTS
SOCIAL
MACHINE DRIVEN
SATELLITE IMAGES
BIO-
INFORMATICS
M2M LOG FILES
SENSORS
VIDEO
AUDIO
BUSINESS DRIVEN
OLTP
ALL DATA TYPES
1X 10X 100X
BIG DATA
TODAY
BIG DATA TOMORROW

9.
Defining Big Data
Any amount
of data
that's too
BIGto be handled by one computer
John Rauser

10.
Why Big Data
12 TB of Tweets in a Day
80%
Of world’s data
is unstructured
30 billion pieces of content
shared on Facebook every
month
Expected Data in 2020 would be
35 ZB
5 Million Trade events per
second
2267 Billion Internet
Users
4.7 billion searches on
Google per day
5 Billion people
tweet,text,call and browse
on mobile phones daily
Walmart handles 1 Million transaction per
hour
255 Million
Websites

11.
Big Data Reference Architecture
Structured Data Sources
Data Integration (Batch /
Near real-time)
Data Repositories
MDM
End User Analytics
Reports / Dashboards
Unstructured/Semi-
structured Data Sources
Web logs, Application /
Network log, Social, Chat
transcripts, Emails
Legacy applications, ERP
and CRM applications
Data Extraction
External feeds
Instrumentation data /
Sensors, RFID, Telematics,
Time and Location data
Real-time Streaming/Integration
Data Cleaning and
Transformation
Change Data Capture for
Structured Data
Change Data Capture
ODS
Analytics
Data Warehouse
DW Appliances
Data Marts
MOLAP CubeIn-memory Databases
Unstructured / Semi-
structured data
Scorecards and Metrics
Events and Alerts
Data Mining and Exploration
Predictive Analytics
Text Analytics
Visual Exploration
Mobile BI
Columnar Databases

12.
Columnar
Databases
Structured Data Sources
Data
Integration
Data Repositories
MDM
End User Analytics
Reports
Unstructured/Semi-
structured Data Sources
Web logs, Application / Network
log, Social, Chat transcripts,
Emails
Legacy and ERP
Data
Extraction,
Transformation
External feeds
Instrumentation data / Sensors,
RFID, Telematics, Time and
Location data
Real-time Streaming /
Integration
Data
Quality
CDC for
Structured
data
Change
Data
Capture
ODS
DW
DW
Appliance
Data
Marts
MOLAP
Cube
In-memory
Databases
Unstructured /
Semi-structured
Scorecards /
Metrics
Events /
Alerts
Data
Mining
Predictive
Analytics
Text
Analytics
HANA / BW
/ Sybase
SAP HANA
Dash
boards
BO WebI /
Crystal
Reports
BO dashboard
Data
Exploration
Mobile
BI
SAP HANA
Sybase IQ /
HANA
BO Mobile
SAP HANA/
Sybase
RDS /
Rapid
Marts
SAP BW
SAP Lumira
SAP Predictive
Analysis
Analytics
Hadoop
Platform
BO CMS
SAP HANA
/ SAP BW
SAP MDM
SAPBO
DataServices
3rd Party
3rd Party
SAP HANA
Big Data Reference Architecture
SAP

13.
Columnar
Databases
Structured Data Sources
Data
Integration
Data Repositories
MDM
End User Analytics
Reports
Unstructured/Semi-
structured Data Sources
Web logs, Application /
Network log, Social, Chat
transcripts, Emails
Legacy Applications
and ERP
Data
Extraction
External feeds
Instrumentation data /
Sensors, RFID, Telematics,
Time and Location data
Real-time Streaming
Data
Quality
CDC for
Structured
Data
CDC for
Unstructured
Data
Hadoop
Platform
ODS
Data
Warehouse
DW
Appliance
Data
Marts
MOLAP
Cube
In-memory
Databases
Semi /
Unstructured
Scorecards /
Metrics
Events /
Alerts
Predictive
Analytics
Text
Analytics
Content
Analytics
InfoSphere
InformationServer
Dash
boards
CognosBuisnessIntelligence
Enterprise
Visual
Exploration
Mobile
BI
Cognos
TM1
Cognos
Mobile
PureData
(Netezza,
InfoSphere
Warehouse)
Cognos TM1
InfoSphere
Data Explorer
SPSS
Premium
SPSS
Content
Analytics
InfoSphere Streams
InfoSphere
CDC
Analytics
Sandbox
Big Insights /
Streams
Big Insights
InfoSphere
MDM
Big Insights /
NoSQL
Big Insights /
HBase
PureData(Netezza,
InfoSphereWarehouse,
ISAS)
Big Data Reference Architecture
IBM

14.
Columnar
Databases
Structured Data Sources
Data
Integration
Data Repositories
MDM
End User Analytics
Reports
Unstructured/Semi-
structured Data Sources
Web logs, Application /
Network log, Social, Chat
transcripts, Emails
Legacy Applications
and ERP
Data
Extraction
External feeds
Instrumentation data /
Sensors, RFID, Telematics,
Time and Location data
Real-time Streaming
Data
Quality
CDC for
Structured
Data
CDC for
Unstructured
Data
Hadoop
Platform
ODS
Data
Warehouse
DW
Appliance
Data
Marts
MOLAP
Cube
In-memory
Databases
Semi /
Unstructured
Scorecards /
Metrics
Real Time
Decision Mgt.
Data
Mining
Predictive
Analytics
Text
Analytics
Data
Integrator
Exadata Dash
boards
BI Publisher
OBI Foundation
Suite
Visual
Exploration
Mobile
BI
Exalytics
OBI Mobile
Oracle/Exadata
Oracle /
Exadata
Essbase /
Hyperion
Exalytics
OBI Scorecard
Exalytics+
OracleREnt.
EndecaOracle Golden Gate
Analytics
Sandbox
Exalytics
Hadoop /
Golden Gate
Big Data
Appliance
Oracle MDM
Big Data
Appliance
Exadata EHCC
/ HBase
Silver Creek
Data Integrator
/ Golden Gate
Real-time
Decisions
Big Data Reference Architecture
ORACLE

15.
Big Data Reference Architecture
Informatica+EMC+SAS
Columnar
Databases
Structured Data Sources
Data
Integration
Data Repositories
MDM
End User Analytics
Reports
Unstructured/Semi-
structured Data Sources
Legacy Applications
and ERP
Data
Extraction
External feeds
Instrumentation data /
Sensors RFID, Telematics,
Time and Location data
Real-time Streaming
Data
Quality
CDC for
Structured
Data
CDC for
Unstructured
Data
Hadoop
Platform
ODS
Data
Warehouse
DW
Appliance
Data
Marts
MOLAP
Cube
In-memory
Databases
Semi /
Unstructured
Scorecards /
Metrics
Data
Exploration
Predictive
Analytics
Text
Analytics
InformaticaPowerCenter&
DataQuality
EMC GreenPlum Dash
boards
SAS BI
Visual
Exploration
Mobile
BI
SAS Visual
Analytics
SAS BI
EMCGreenPlum
Database
EMC GreenPlum
SAS OLAP
Server
SAS Visual
BI
SAS Ent.
Miner
SAS Strategy
Mgt
JMP Pro
SAS Text
Miner
Informatica PowerCenter – Real-time edition
Analytics
Sandbox
EMC GreenPlum
UAP
Informatica
hParser /
Hadoop Pwx
EMC
Greenplum HD
EMC
GreenPlum
HD
HBase
Informatica
MDM
Web logs, Application /
Network log, Social, Chat
transcripts, Emails

16.
Big Data Reference Architecture
Open Source Technologies
Columnar
Databases
Structured Data Sources
Data
Integration
Data Repositories
MDM
End User Analytics
Reports
Unstructured/Semi-
structured Data Sources
Legacy Applications
and ERP
Data
Extraction
External feeds
Instrumentation data /
Sensors RFID, Telematics,
Time and Location data
Real-time Streaming
Data
Quality
CDC for
Structured
Data
CDC for
Unstructured
Data
Hadoop
Platform
ODS
Data
Warehouse
DW
Appliance
Data
Marts
MOLAP
Cube
In-memory
Databases
Semi /
Unstructured
Scorecards /
Metrics
Predictive
Analytics
Text
Analytics
ApacheMapReduce,Pig,
TalendDataIntegration&DataQuality
Commercial
Product
Dash
boards
Visual
Exploration
Mobile
BI
Apache Derby
PentahoMob
ile BI
MySQL,Apache
Hive
MySQL, Hive
SAS OLAP
Server
R, Apache
Mahout
SAS Text
Miner
Apache Flume
Analytics
Sandbox
Apache HDFS +
R
Apache
Hadoop
HBase,
NoSQL
HBase
Talend MDM
Web logs, Application /
Network log, Social, Chat
transcripts, Emails
Pentaho
BusinessAnalytics,BI

17.
What is Hadoop
• It’s a framework for large-scale data processing:
• Inspired by Google’s architecture:
• A top-level Apache project – Hadoop is open source
• Written in Java, plus a few shell scripts
• An open-source software framework that supports data-intensive
distributed applications
• Abstract and facilitate the storage and processing of large and
rapidly growing data sets
• Structured and non-structured data
• Simple programming models

18.
2 key components of Core Hadoop

19.
• Yahoo! : More than 100,000 CPUs in ~20,000 computers running Hadoop; biggest cluster: 2000 nodes
(2*4cpu boxes with 4TB disk each); used to support research for Ad Systems and Web Search
• AOL : Used for a variety of things ranging from statistics generation to running advanced algorithms for
doing behavioral analysis and targeting; cluster size is 50 machines, Intel Xeon, dual processors, dual
core, each with 16GB Ram and 800 GB hard-disk giving us a total of 37 TB HDFS capacity.
• Facebook: To store copies of internal log and dimension data sources and use it as a source for
reporting/analytics and machine learning; 320 machine cluster with 2,560 cores and about 1.3 PB raw
storage;
• FOX Interactive Media : 3 X 20 machine cluster (8 cores/machine, 2TB/machine storage) ; 10 machine
cluster (8 cores/machine, 1TB/machine storage); Used for log analysis, data mining and machine
learning
• NetSeer - Up to 1000 instances on Amazon EC2 ; Data storage in Amazon S3; Used for crawling,
processing, serving and log analysis
• Powerset / Microsoft - Natural Language Search; up to 400 instances on Amazon EC2 ; data storage
in Amazon S3
Hadoop uses every where

20.
HDFS : High level architecture
• HDFS Follows a master-slave architecture
• 2 Major Daemons in HDFS –
• Name Node
• Data Node
• Master : Name Node
• Responsible for namespace and metadata
• Namespace : file hierarchy
• Metadata : ownership, permissions, block locations etc
• Slave : DataNode
• Responsible for storing actual data blocks

21.
MapReduce : High Level Architecture
• Map reduce has a master slave architecture too
• 2 Daemon processes
• Master : Job Tracker
• Responsible for dividing, scheduling and monitoring work
• Slave : Task Tracker
• Responsible for actual processing

22.
High Level View

23.
Apache Hadoop Ecosystem

24.
Disruptions

25.
1 Japanese dating app

26.
2.Heart implants

27.
MOOC
3

28.
Sensored cows in Netherland

29.
Googles autonomous car

30.
What's common to the following game changing solutions ?
1
2
3
4 5
Japanese dating app
Sensored cows in Netherland Googles autonomous car
MOOC
Heart implants

31.
At the core there is a deep
embedded DATA PRODUCT !

32.
Created by DATA SCIENCE !
Conquer the world ! Become Data Scientist

33.
• How our health gets cared
for ?
• How we learn ?
• How we fall in love ?
• How we do farming ?
• How we drive ?
The world around is changing… Our lives are intimately Surrounded by Data products
(an intimate fabric of our lives)

34.
• Amazon Defeated Borders ( Books )
• Netflix Defeated Blockbuster ( Video )
• iTunes Defeated Tower records ( Music )
• Google defeated Yahoo ( Search ) – Page rank algorithm
How did the following players disrupt the Marketplace ?

35.
If Data Science is not integral you are no longer in the game

36.
Demystifying
Data Science
( in simple plain everyday English  )

37.
In a Nutshell
• Data Science is the extraction of knowledge from data
• Data Science is the art of turning data into actions
• The ability to take data—to be able to understand it, to process it,
to extract value from it, to visualize it, to communicate it
• Data Science seeks to
• Extract meaning from data
• Create " Data Products"
• Use all available data to tell a valuable story to non- practioners
The future belongs to the companies and people that
turn data into products

38.
Data Science is every where

39.
40
Known Unknowns
(BI)
Unknown Unknowns
( Data Science )
Lots of $ impacting patterns
Unnoticed
Waiting to be discovered!
Data Science vs.BI

40.
“As is” state in most organizations
Data
( Sales , Finance )
Reports
( BO, Cognos, MSAS )

41.
“As is” stage with leading game changers
Data repository
Insights
Analytics cell + Modeling processes
( Segment, Score, Text mine )
Move from Reports  Insightful Actions that Impact

42.
What's are 4 core differences between Data Science & Dashboards ?
Data repository
Dashboards
Data repository
(Purchase habits)
Signal
(Similiar people discovery)
ML process
(Collaborative filtering)
Actions
(Recommend a product )
Outcomes
(Improve cross sell)
2
3
4
Dashboards
1
ML + Signals + Actions = Game Changing Outcomes

43.
What exactly is an model ?
• Mathematically defining a real world phenomena
• Representative of real world
• For example cross sell model

44.
What are 3 common things between
predictive models and caricatures ?
• Its an approximation, not
a perfection
• Its better than not having
anything
• It get the job done
REAL WORLD
ANALYTICAL MODEL

45.
Use data
to discover Signals (patterns)
that cause changes
that impacts $ .
What's the Goal of Data Science ?

46.
Data Science Reference Architecture – Key components
Hadoop
Hive
Hana
Info bright
Clustering
Text mining
Mobile
Digital
Data Ingestion Pipeline

47.
Machine Learning Reference Architecture
STORE
( Hadoop, Hive, HANA, Cloudera, Splunk, Hortonworks)
SENSE
( signal extraction- text mining, scoring models ),
RESPOND
( Front line actions thru website, call centre )
1
2
3

48.
Snapshot of Machine Learning Techniques
1. Segmentation
3.Forecasting
5. Scoring models
2.Text mining
4. Visual Analytics
6.Optimisation
1. Customer behavior segmentation
2. Defect segmentation
3. Employee segmentation model
4. Supplier segmentation mode
5. “Chunking” groups
6. Discovered by algorithm
1. Convert messy unstructured text into actionable signals
2. Keyword frequencies
3. Sentiment ratios
4. Blogs
5. Call center transcripts
6. Emails
7. Multi channel sentiment analysis
1. Predict CLTV
2. Predict Sales at a neighborhood outlet
3. Predict Salary based on experience, qualification,
rating, market demand
4. Identify drivers of behavior
5. Weights processing
1. Beyond line, bar , pie charts
2. Geospatial modeling to see geo correlation
3. Spread analysis
4. Outlier detection
1. Churn propensity
2. Cross sell
3. Attrition modeling in HR
4. Risk scoring models in Banking
5. Logistic
6. Neural networks
7. Decision trees
8. Support Vector machines
1. Constraint modeling
2. Maximize an outcome
3. Maximize sales without cannibalizing sister brands

49.
Its all about DETECTING PATTERNS !

50.
1. Segmentation

51.
2. Unstructured Text Mining

52.
Real world Unstructured text mining in
health care
Doctors transcripts
Split sentences
onto
words/tokens
Step-1 : SPLIT
Filter “noise”
words eg : I ,
the, is, was,
Step-2 : FILTER ‘Pulmonary’=
‘pulmonar’
‘Insomnia’ = ‘Sleep’ =
‘Sleeplessnes;
‘
Step-3 : STEMMING
Keyword extraction &
Theme generation
Step-4 : THEME EXTRACTION
Step-5 : THEME /
KEYWORD ANALYSIS
Lab diagnostics Nurses Observations
Cardiac
watch list
Oncology
watch list
Pulmonary
watch list
Diabetic
watch list
Schizophreni
a watch list

53.
3. Scoring Models

54.
4. Forecasting !

55.
5. Recommenders

56.
Industries disrupted by Data Science
• Infrastructure optimisation, Network securityTelecom
• Customer sentiment, Multi channel analysisBanking
• Consumer engagement, Recommendation enginesDigital channel
• Autonomous cards, Fords OnStarAutomotive
• WearablesHealth care
• Operations optimisationOil n Gas
• DigitisationRetail

57.
What factors are driving companies towards data science ?
• Competitive advantage in the market place ( get ahead fast using unique insights )
• Existential threat ( others are moving ahead fast and I need to catch up )
• Revenue enhancement ( Cross sell models, recommenders )
• Cost optimisation ( Operational efficiency )

58.
Technology behind Data Science
Algorithams
Machine learning
Predictive
analytics
R

59.
Why is Big Data HOT ?

60.
Big Data jobs are Exploding!

61.
Data Science jobs are Exploding!

62.
Data Science Jobs exploding in India too !

63.
1
2
3

64.
Transform yourself to 21st Century Skills

65.
The 6 Most Desired Skills in 2015

66.
1
2
3
To summarize
3 key takeaways …

67.
FAQ

68.
FAQ-1: “I am confused between Hadoop and Data Science …
What's difference between Hadoop and Data Science?”
• Hadoop = Data Infrastructure layer
• Data Science = Sensing patterns from data to impact business outcome

69.
FAQ-2 : “I have worked on SAP, Oracle, etc How do I transition
to becoming a Data Scientist ?”
• Execute your first Data Science pilot
• Step-1 : Learn R
• Step-2 : Zero in on a business problem to solve
• Step-3 : Setup R Your technology connector …Get access to data from your
Technology
• Step-4 : Apply an Analytical construct ( VEDA ML )
• Step-5 : Discover the pattern which impacts the outcome
• Step-6 : Present final results to executive business team
• Explore setting up a Data science project within existing organisation
• Meetups to explore the outside world

70.
FAQ-3: “Should I know probability and advanced statistics ?”
• Not really
• We are focussed on APPLICATION and not THEORY underpinning it
• We will teach you
• Business problem to solve
• How to execute the command on a platform
• What to look for in the output
• What happens within the black box can be seen later

71.
FAQ-4: “This is a big shift for me … In your experience how long
does it take to make the transition from IT to Data Science ?”
• We have seen people make the transition from 4 weeks to about 6 months
• It depends upon the time + passion + drive you have

72.
FAQ-5: “How are we going to prepare you for the data science
job market ?”
1. Mock preparatory sessions
2. Worksheets + Modelling Checklists + Data Science Playbooks
3. Live projects on clustering , scoring which can be put in resume
4. Our strategic tie-ups with Organisations looking for data science skills
5. Top 30 Practitioner generated Data Science questions

73.
FAQ-6: “I am not an IT professional but a domain person. How
can I get started ?”
1. Option-1 : Focus on Industry use cases
2. Option-2 : Take basic introduction to data sciences

74.
Big Data Resources• datasciencecentral.com
• bigdatauniversity.com
• Courseera.com
• Big Data Architecture
• Spotting Signals in Big Data
• Signal Extraction Methodology
• Advanced Visualization in Big Data
• Exploratory Data Analysis (EDA) : Quick Deep Dive
• Best practices in designing dashboards and scorecards
• Exploring Big Data Using Bivariate Analysis
• Where to start looking in Big Data using Univariate Analysis
• Big Data Platform & Applications
• Statistics Role in Data Science
• Applied Mathematics Role in Data Science
• Data-Scientist-playbook
• 5-disruption-data-products By Data Science

75.
All The Best
Happy Hadooping & Dating with Data Science
Conquer the world !
Become Data Scientist