Becoming a data driven organization

New IT Agenda
Provide Access To
All Applications & Data Through
Mobile Devices.
Use Adaptive, Data-Driven
Security To Rapidly Respond To
Emerging Threats.
Build A Data Lake To Deliver
Insights And Applications On
All Data.
Move To A Software-Defined
Data Center Infrastructure And
Expand It To A Hybrid Cloud.
2
Use Agile Development To
Build New Customer-Centric
Applications.
Balance Risk
Cut Operational Costs &
Legacy More Than Ever
React Faster To
Find New Growth
Today’s Business
Challenges

Best Of Breed. Architected Horizontally, Not Vertically. Choice.
Our Strategy: Build A Differentiated Stack
BIG DATA SOLUTIONS
PLATFORM AS A SERVICE
AGILE APPLICATION DEVELOPMENT
INFORMATION INFRASTRUCTURE
CONVERGED INFRASTRUCTURE
SOFTWARE-DEFINED DATA CENTER
HYBRID-CLOUD, MOBILITY
ADVANCEDSECURITY

Many Industries Face Structural Change

Volvo Cars – Big Data app & service
Deliveries to your car – Roam Delivery

20,772 HARD DRIVES
300 PALLETS
8 TRUCKS
85 PETABYTESSOLD INTO ONE WEB-SCALE PROVIDER IN ONE ORDER

Capacity
Performance
Low Service
Level
High Service
Level
Performance “Good Enough”
Capacity Optimized ($/GB)
Data Loss Not A Disaster
Consistently Good Performance
Eventual Consistency Of Data
Data Loss Not A Disaster
Performance “Good Enough”
Capacity Optimized ($/GB)
Data Loss A Disaster
Consistently Good Performance
Consistent Data
Great Performance
Consistent Data

The Core is Super-Scaling

The Edge is Hyper-Extending

TODAY’S DATA CENTER SOFTWARE-DEFINED DATA CENTER
TRADITIONAL APPLICATIONS NEXT GEN CLOUD APPLICATIONS
120M
-2016-
91M
-2013-
34M
-2016-
11M
-2013-
TRADITIONAL APPLICATION GROWTH NEXT GEN CLOUD APPLICATION GROWTH

THE 3RD PLATFORM REDEFINES EVERYTHING

BUILT FOR THE SPEED OF BUSINESS

21Pivotal Confidential–Internal Use Only 21Pivotal Confidential–Internal Use Only
Data Driven Application
Development

22Pivotal Confidential–Internal Use Only
Pivotal
At-a-Glance
Ÿ  New Independent Venture: Spun out &
jointly owned by EMC & VMware
Ÿ  Deep Execution Talent: 1700 employees
Ÿ  Proven Leadership: Paul Maritz, CEO
Ÿ  Global Customer Validation:
+1000 Tier-1 Enterprise Customers
Ÿ  Strategic Backing: $100M investment by GE
Ÿ  Bold Vision: New platform for a new era,
focused on the intersection of apps, big data
and analytics

Need for Speed
Ÿ  Enterprises are being driven to compete, innovate & execute
faster than ever before:
–  Global reach and emerging markets
–  Ever-increasing customer expectations
–  Legacy environment & cost pressures
Ÿ  At a time when we’re witnessing the most disruptive platform
shifts and advances in technology in over 30 years
Ÿ  Every business is quickly becoming a software business
Ÿ  Software is how companies engage with customers, powered
by new data insights and new
Social
Cloud
Big
Data
Mobile

What Matters: Apps. Data. Analytics.
Apps power businesses, and
those apps generate data
Analytic insights from that data
drive new app functionality,
which in-turn drives new data
The faster you can move
around that cycle, the faster
you learn, innovate & pull
away from the competition

“Software is Eating the World”

Software is Changing Industries
$3.5B valuation
Financial Services
$3.5B valuation
Travel & Hospitality
$3.5B valuation
Transportation
$3.2B Acquisition by Google
Home Automation
$20B valuation
Entertainment
$1.1B acquisition
Monsanto--Agriculture

We need to innovate or we die. Pivotal and Cloud Foundry is
our big bet to leapfrog the competition
With Cloud Foundry, we built what looks like a software
company… Moving from silos to a single platform.
Cloud Foundry's potential to transform business is vast…
companies leveraging software will outperform their peers
Enterprises Must Become Great at Software

Francisco Gonzalez
CEO at BBVA
“Banks need to take on Amazon and
Google or die. The shift to digital
requires a complete overhaul of
banks technology…it is a matter of
survival.”

Rapid Execution Requires a New Approach
•  Agile teams and rapid iteration
•  Continuous delivery without downtime
•  Horizontally scalability (data and app)
•  Standardized service binding and
discovery
•  First class Mobile support
•  Deep user analytics
Development
Delivery
Operation
Iteration

Jonathan Rosenberg
CTO & VP, Collaboration
“PaaS is the operating system for
the cloud. As the set of APIs and
services for PaaS's grow, the choice
of PaaS becomes more crucial as the
costs of porting go up. This is one of
the benefits of open source PaaS
offerings like Cloud Foundry.”

Is Your Enterprise Ready?

Fail, Learn, Adapt, Repeat.
6 Months to 6 Weeks
$1.1M Saving per App

Incredible Cloud Foundry Ecosystem

The Cloud Foundry Foundation
more to come…
“This is a significant announcement for PaaS in general and for
Cloud Foundry in particular. It potentially signals a
consolidation that is going to become apparent…predicts that
Red Hat will shutter OpenShift and throw its hat in with Cloud
Foundry within the year.” - Forbes, Ben Kepes, 2/24/14

Elastic Runtime
Java, Spring, Ruby, Node.JS
Built-in “Middleware” Services
Operation Manager
Installation, Management,
Monitoring, Upgrades/Updates
...ETC
Pivotal Approach: Your Platform for Building Great
Software
PivotalOne
Pivotal One
CO-INNOVATION
Agile Software Development
Data Lake Solutions
PIVOTAL
MySQL
Pivotal One
SERVICES

Pivotal Approach: An Application Centric World
Infrastructure Specific
JVM
VM
Pre-Provisioned Pool of VMs
Container 1
App Server
JVM, etc..
Container 2
App Server
JVM, etc..
App1 Common Access Tier (App1, App2)
App Server
Configurations Built-in Middleware Services
JVM
VM
App2
App Server
Configurations
IaaS Agnostic

GE Capital Builds Foundation For Value Add Insights
“ Critical Insights and data is deleted
because it’s too expensive to store.”
“We need the ability to blend data
fabric, build analytics, and create
applications on top of this.”
“Access any internal or external
data of interest through a familiar
interface.”
“Now we analyze Social Media to
predict trends, and help dealers
make decisions.”

Use-Case: Data and PaaS Drives Business Agility
Pivotal CF Operation Manager
Any Infrastructure
Big/Fast Data
Real-time change to customer-facing
application based on data analysis
Deploy/Update
(Private/Public)

Spring becomes the enabler
Deploy to
Cloud or on
premise
Big,
Fast,
Flexible
Data Data
Processing,
Integration
Spring Data
•  JPA/JDBC
•  MongoDB
•  Redis
•  Neo4j
•  GemFire
•  Data REST
•  Spring
Hadoop
•  Spring
Integration
•  Spring
Batch
•  CloudFoundry
•  vCloud Suite
•  Google App
Engine
•  Amazon Elastic
Beanstalk
•  CloudBees

Data Driven: Harder Than it Sounds
Operationalize
Ingest
Distill
Interface
Process
Analytical Transactional
Operationalize
Ingest
Distill
Interface
Process
Operationalize
Ingest
Distill
Interface
Process
Real Time Near Real Time Batch
Predictive call routing, fraud
prediction, dynamic pricing,
re-marketing, stream analytics
Analytic model designs, transaction
analysis, trend analysis
ETL, archive, trending, monthly and
weekly jobs

Data Driven: Impossible in Silos
Finance Manufacturing Marketing IT
Data Growth Over 60%
Floods These Silos

One Platform, Multiple Use Cases
Flexibility to expand the
ingestion of network data as
the underlying infrastructure
changes – easy expansion into
4G LTE.
External
data sources
•  Linked-in
•  Twitter
•  Facebook
•  Weather
•  …
Internal
data sources
•  CRM
•  EDW
•  Customer Portals
•  …
Mobile Network Infrastructure
Intelligently set triggers at the
edge of the network that looks
for 'interesting' events that
require instant action
Build
New Apps
Integrate
Existing Apps
Agile approach to enable apps to access in real-time
All
History
Current
Status
Predicted
Intelligence
Single Unified Platform
•  Capture everything
•  Real-time data processing
•  Single version of the truth
•  End-to-end visibility across
different data sources
•  Scalable and cost effective

RTI In Telco Ecosystem
RTI-T
Integration
Sanity Check
Filter
Enrich
Transform
Apply Logic
OSS
BSS
Network
Elements
Application
Hosting
Environment
for Real-time
Use-case
Realization
Network
Optimization
Customer
Experience
Management
....
Ingest DistributeProcess
Route
Persist
Long Term Storage
Operational
Datastore
Analytics

Solution Architecture
RTI-T
Integration
Sanity Check
Filter
Enrich
Transform
Apply Logic
OSS
BSS
Network
Elements
Cloud
Foundry
Network
Optimization
Customer
Experience
Management
....
Ingest DistributeProcess
Route
Persist
Pivotal Data Lake
GemFire XDADS/HAWQ

World’s Leading Experts
Pivotal Labs – Pivotal Data Labs
Pivotal One
Pivotal Application Suite
BATCH BATCH
NEAR TIME NEAR TIMEHAWQGreenplum DB
Pivotal HD
REAL TIME REAL TIMEGemFire XDGemFire

Pivotal’s Opportunity
Uniquely positioned to help
enterprises modernize each
facet of this cycle today
Comprehensive portfolio of
products spanning Apps, Data
& Analytics
Converging these technologies
into a coherent, next-gen
Enterprise PaaS platform

© Copyright 2014 Pivotal. All rights reserved.
It is not the strongest of the
species that survives, nor the
most intelligent that survives.
It is the one that is most
adaptable to change.
“
”-Charles Darwin
BE PREPARED FOR
THE SPEED OF BUSINESS

Ÿ  What is the Pivotal platform?
Ÿ  Why is it so cool?
Ÿ  What amazing things have we done with it?
BE PREPARED FOR
THE SPEED OF BUSINESS

Evolving Enterprise Data Architecture
Analytic
Data Marts
MPP Database
Operational
Intelligence
In-Memory DB
Run-Time
Applications
In-MemoryObject
EnterpriseData
Warehouse
RDBMS
Data Staging
Platform
TraditionalBI/Reporting
Data Visualization
Data
Ingestion
System
Stream/CEP

Analytic
Data Marts
Operational
Intelligence
Run-Time
Applications
EnterpriseData
Warehouse
Data Staging
Platform
TraditionalBI/Reporting
Data Visualization
Data Ingestion
System
Pivotal Data Product Portfolio

Why is the Pivotal platform so awesome?
Infrastructure
Independent
Fast &
Scalable
Schema
Free
Easy to
Use
Real
Time

Big Data & Data Science
Decision

=

Data

+

Rules

“Big
Data”
Data

Science

Social
Media

Commercial
&

Public
Data

Dark
Data

“Big Data”
Opera7onal

Data

Combining data sources: Example
IPSQ (Quality)
Owner: TS Production team
Test flags from production line
1 year ~300GB
APDM
Owner: TS Production team
Full vehicle history including IPST (technical),
IPSL (logistics), IPSQ test flags and all test
results.
30 years ~TBs
FASTA
Owner: Aftersales
Dealership electronic tests
Identifies early issues with cars
>25TB
IQS: Initial Quality Survey from JD Power
Owner: R&D
Survey responses from new owners after 90
days for approx 1700 vehicles
Few thousand lines ~MB
Social Data
Owner: R&D
Pulling 500MB per day from Twitter
TQP
Owner: Supplier management
PDFs of parts spec sheets
~ 500GB

Generating value from data:
Car configurator example
Sales
Configurations
Customers
Sales
Configurations
Customers
Basic
Recommendation
Engine
Sales
Configurations
Customers
Advanced
Recommendation
Engine
Sales
Configurations
Customers
Yield
Optimization
Engine
All car elements:
•  Attribute frequencies (colors etc)
•  Attribute combination frequencies
For instance:
•  Browsing history
•  Usage patterns
•  Demographic insights
Ideally:
•  Volumes
•  Pricing
•  By market
•  Linkable to configurations

Traditional
Systems “Big Data”“Fast Data”
The value of data over time
Time
Value of
Data ($)
µs ms s hour day month year yr+
Pivotal Data
Science Labs

"Pivotal is aiming for the
enterprise market that's
realizing that software
is the biggest
differentiator in
any industry."
— Larry Dignan, ZDNet
“The number of companies that
have bought into the initiative, the
amount of code being contributed,
the customer wins that
ecosystem members are
enjoying suggest that Cloud
Foundry is preeminent among all
the open source PaaS initiatives."
— Ben Kepes, Forbes
"If you're in the business of
building enterprise software,
scrambling to figure out what
your company is doing around
big data and analytics, mobile
and the cloud, then there's a fair
chance you'll want to pay
attention to Pivotal."
— Arik Hesseldahl, WSJD
But don’t just take our word for it…

© Copyright 2014 Pivotal. All rights reserved.© Copyright 2014 Pivotal. All rights reserved.
What does
traffic data
look like?

…like this?

…or this?
(Note: This is the
least offensive topic
cluster in our
Twitter data!)

Velocity by Time of Day

0 50 100 150 200
0.0000.0050.0100.0150.0200.0250.030
density
Link 1000064869
km/h
Velocity Distribution

Gaussian Mixture Model
0 50 100 150 200
0.0000.0050.0100.0150.0200.0250.030
density
Combined
Component 1
Component 2
Component 3
Component 4
Link 1000064869
km/h
0 50 100 150 200
0.0000.0050.0100.0150.0200.0250.030
density
Combined
Component 1
Component 2
Component 3
Component 4
Link 1000064869
km/h
0 50 100 150 200
0.0000.0050.0100.0150.0200.0250.030
density
Combined
Component 1
Component 2
Component 3
Component 4
Link 1000064869
km/h
0 50 100 150 200
0.0000.0050.0100.0150.0200.0250.030
density
Combined
Component 1
Component 2
Component 3
Component 4
Link 1000064869
km/h

Decision Trees
Example
0 50 100 150 200 250
0.000.010.020.030.04
density
Combined
Component 1
Component 2
Component 3
hour >= 14
hour < 20
weekday = 1,2,3,4,5 weekday = 1,2,3,4,5
weekday = 1,2,3,4,5
nextlink = −1,100000
< 14
>= 20
6,7 6,7
6,7
100002
1
0.47
1
0.69
1
0.76
1
0.81
3
0.85
2
0.55
2
0.73
3
0.65
2
0.56
2
0.63
2
0.68
3
0.49
3
0.73

Sneak Peek at our TfL Data Demo
Ÿ  Used the freely
accessible TfL data
for a demo
Ÿ  Shows # of active
disruptions over
different days in
London
Ø  Rush hour effects visible
Ø  Nights are more quiet, but more disruptions on weekend nights

Kaiser Permanente Hackathon
Insight
Patient Application

Kaiser Permanente Hackathon

Text Analytics for Churn Prediction
Customer
A major telecom company
Business Problem
Reducing churn through more accurate
models
Challenges
Ÿ  Existing models only used structured
features
Ÿ  Call center memos had poor structure and
had lots of typos
Solution
Ÿ  Built sentiment analysis models to predict
churn and topic models to understand topics
of conversation in call center memos
Ÿ  Achieved 16% improve in ROC curve for
Churn Prediction

Predicting Commodity Futures through Twitter
Customer
A major a agri-business cooperative
Business Problem
Predict price of commodity futures through
Twitter
Challenges
Ÿ  Language on Twitter does not adhere to
rules of grammar and has poor structure
Ÿ  No domain specific label corpus of tweet
sentiment – problem is semi-supervised
Solution
Ÿ  Built Sentiment Analysis and Text
Regression algorithms to predict commodity
futures from Tweets
Ÿ  Established the foundation for blending the
structured data (market fundamentals) with
unstructured data (tweets)

Network Intrusion Detection
Customer
One of the worlds largest health care providers
Business Problem
Detect advanced cyber threats in a large
heterogeneous environment and to reduce
malware ‘free-time’
Challenges
Ÿ  Covert threats employ advanced techniques to
bypass traditional security appliances.
Ÿ  Last year 416 days was the median number
before detection on a compromised network.
(Source: Mandiant)
Solution
Ÿ  Built a new behavioral intrusion detection
framework based on machine learning, graph
theory and security research
Ÿ  Designed operational components of their next
generation SIEM.
Firewall
•  Engineered a full
featured custom
social graph based
intrusion model
•  Identified breaches
not a single security
product they owned
was able to detect

Website Classification
Customer
An internet domain name service provider
Business Problem
Create a multilevel website classification that
groups websites by function rather than topic
Challenges
Ÿ  Complex unstructured data format required
several transformations
Ÿ  Model needed to be language independent, so
classic language features could not be used
Solution
Ÿ  New hierarchical model resulted in reducing the
number of previously ‘unclassified’ websites by
~75%
Ÿ  Created an in-database analytics framework for
unsupervised learning models and enabled real-
time validation of current production model
Map of Domains

Pivotal’s Platform
Uniquely positioned to help
enterprises modernize each
facet of this cycle today
Comprehensive portfolio of
products spanning Apps, Data
& Analytics
Converging these technologies
into a coherent, next-gen
Enterprise PaaS platform

Infrastructure for Data
Driven Workloads

PLATFORM
AS A SERVICE
VIRTUAL
WORKSPACE
BUSINESS
DATA LAKE
SECURITY
ANALYTICS
SOFTWARE
DEFINED
DATA CENTER
SERVICEPROVIDER
ENTERPRISEDATACENTERA Unique Federation Of Companies
Delivering The Software-Defined Enterprise. Solutions & Choice.
Partners
BIG DATA SOLUTIONS
PLATFORM AS A SERVICE
AGILE APPLICATION DEVELOPMENT
SOFTWARE-DEFINED DATA CENTER
HYBRID-CLOUD, MOBILITY
INFORMATION INFRASTRUCTURE
CONVERGED INFRASTRUCTURE
ADVANCEDSECURITY

Converged
Infrastructures
Partners
vCloud
Hybrid Service
Hybrid Cloud
Managed As One Cloud
Federation Solutions
5 Solutions Enabling The Software-Defined Enterprise
Next Gen Cloud Apps
PLATFORM
AS A SERVICE
SOFTWARE-DEFINED
DATA CENTER
VIRTUAL
WORKSPACE
BUSINESS
DATA LAKE
SECURITY
ANALYTICS
SOFTWARE-DEFINED
DATA CENTER
VIRTUAL
WORKSPACE
PLATFORM
AS A SERVICE
SECURITY
ANALYTICS
BUSINESS
DATA LAKE

Hadoop Overview
Hadoop
is an open-source framework from Apache that allows for
parallel batch processing of very large data sets
MapReduce
is the Hadoop process that divides the workload so
multiple devices can process it
HDFS
is the file system for the data. It provides data protection
and locality with multiple mirrors (usually 3 times)

Isilon Scale-Out NAS Architecture
OneFS Operating
Environment
Intra-cluster
Communication Layer
Client/Application Layer Ethernet Layer
SingleFS/Volume
CIFSNFS
FTPHTTP
HDFS for
Hadoop
REST for
Object
Gig-e
10 Gig-e
Network
Protocols

Traditional “Share-Nothing” Hadoop
Existing Virtualized Data Center SHARE-NOTHING Hadoop Infrastructure
Unstructured Data
1
Existing Primary Storage
2 3 4 2 3 4 2 3 4 2 3 4
•  Hadoop replication count
(R=3) means 4 data copies
•  Data has to copy to the
Hadoop cluster before analysis
can begin (Time to Results)
How will you maintain data
consistency when a file changes
on your primary storage?

Existing Virtualized Data Center
Isilon “Share-Everything” Hadoop
1
Ÿ  Start using Hadoop NOW with
unused processing and RAM
available in your VMware
environment
Ÿ  No replication required
(Use your existing data)
Ÿ  Access to same data via NAS
and HDFS protocols
Ÿ  Time to results extremely fast
using already existing data with
NO COPIES or wasted $
Analysis Can
Begin with the
1st VM
New Hadoop Compute Nodes
Unstructured Data
Use Native HDFS Protocol

Ethernet
Job Tracker Task Tracker DataNode 2nd NameNode
NameNode
Hadoop Architecture - Traditional
R (RHIPE) Mahout Hive HBasePIG
NameNode
Data Node + Compute Node

Ethernet
R (RHIPE)
PIG
Mahout Hive HBase
Job Tracker Task Tracker DataNode
Compute Node Compute Node Compute Node
Compute NodeCompute Node Compute Node
NameNode
Hadoop Architecture with Isilon
name
node
name
node
name
node
name
node
datanode

HDFS
SMB, NFS,
HTTP, FTP,
HDFS
Node
reply
Node
reply
Node
reply
Node
reply
NameNode
Data
Support for Multiple Hadoop Distributions
name
node
name
node
name
node
name
node
datanode
NFS
SMB
SMB
NFS
MAP Reduce
MAP Reduce
MAP Reduce
MAP Reduce
MAP Reduce
MAP Reduce
MAP Reduce
MAP Reduce
MAP Reduce
MAP Reduce
MAP Reduce
MAP Reduce
MAP Reduce
MAP Reduce
MAP Reduce
MAP Reduce
MAP Reduce
MAP Reduce

Dependent Scaling Traditional Hadoop HDFS
Isilon HDFS
Ÿ  Storage to Compute ratio is fixed
Ÿ  Scaling compute means scaling
capacity
Ÿ  Difficult to provide QoS
Ÿ  Compute upgrade is a forklift
Ÿ  Scale compute independent of
storage
Ÿ  Achieve optimal performance
balance even as workloads evolve
Ÿ  No data migrations, ever!
Ÿ  Add new performance as
hardware evolves
Compute
Storage
Required
performance/
capacity
Required Hadoop
Cluster Nodes

Independent Scaling Traditional Hadoop HDFS
Isilon HDFS
Ÿ  Storage to Compute ratio is fixed
Ÿ  Scaling compute means scaling
capacity
Ÿ  Difficult to provide QoS
Ÿ  Compute upgrade is a forklift
Ÿ  Scale compute independent of
storage
Ÿ  Achieve optimal performance
balance even as workloads evolve
Ÿ  No data migrations, ever!
Ÿ  Add new performance as
hardware evolves
Compute
Storage
Required
performance/
capacity
Required Hadoop
Cluster Nodes

Snapshot & Version Control Before
After
Ÿ  Traditional HDFS does not have
replication
Ÿ  No Snapshotting of data
Ÿ  Loss of version control
Ÿ  Not designed for Mission Critical
Ÿ  Full SnapshotIQ integration
identifies changes
Ÿ  Multi-threaded, Multi-Node Scale-
Out replication
Ÿ  Improved RPO/RTO for business
continuity
Ÿ  Geo-replicated Hadoop!

Data Center Network
Time-to-Results
Data Copy Analysis In-Place Analysis
Hadoop on a Stick
Have you ever
copied 100TB from
Primary Storage to
a Hadoop system?
How long does it
take to copy
100TB from one
place to another
over a 10Gb link?
>24 Hours
Data Center Network
Hadoop Compute Nodes
Reading
relevant
data to
analysis

A real world example
Cost Comparison
Customer requirements
Ÿ  640 TB raw capacity
Ÿ  64 Compute (1 per 10TB)
DAS Option
Ÿ  14.8% usable capacity/DataNode
Ÿ  38 racks of servers
Isilon Option
Ÿ  10 Racks (including Compute)
Ÿ  65% less expensive than DAS
Hadoop on Isilon is often significantly less costly!
Network
Hadoop
Licensing
Management
Config
Installation
Energy
Isilon
Servers
$ 0
$ 1M
$ 3M
$ 4M
$ 5M
$ 6M
$ 2M

Efficiency and flexibility
The Isilon Advantage for Hadoop
Ÿ  No data ingest necessary
Ÿ  Eliminate 3x mirroring
Ÿ  Over 80% storage utilization
Ÿ  SmartDedupe to further reduce storage needs by up to 30%
Ÿ  Scale compute and data independently
Ÿ  Multi-protocol access
Ÿ  Simultaneous multi-distribution support
Ÿ  Ability to leverage VMware vSphere Big Data Extensions to reduce
datacenter footprint, power, space, and cooling

Data protection and security
The Isilon Advantage for Hadoop
Ÿ  Highly resilient architecture
Ÿ  Robust data protection options
(DR, snapshots, etc.)
Ÿ  Eliminate NameNode single point of failure
Ÿ  SEC 17a-4 compliant WORM
Ÿ  Kerberos authentication
Ÿ  Hadoop multi-tenancy

How Do I Start Using Hadoop?
EMC Hadoop Starter Kit (HSK)
Ÿ  Visit https://community.emc.com/docs/DOC-26892
Ÿ  Watch the demo video
Ÿ  Follow the instructions to deploy Hadoop to your existing
Isilon and VMware infrastructure in about an hour
Ÿ  There are customized HSKs for Apache, Pivotal, Cloudera,
and Hortonworks

Becoming a data driven organization

Becoming a data driven organization

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