2014 Big_Data_Forum_Pivotal

1© Copyright 2014 Pivotal. All rights reserved. 1© Copyright 2014 Pivotal. All rights reserved.
Building the
Platform for the
Internet of Things
PIVOTAL – A NEW PLATFORM FOR A NEW ERA
Melissa Ries,
VP & GM Pivotal, APJ
mries@gopivotal.com
Tweet_MJR40

2© Copyright 2014 Pivotal. All rights reserved.
Business Expectations Exceed IT Capabilities
Cloud, Big Data, IoT
Social, Mobile
IT Technology Eras
Mainframe
Back Office
Client-Server
Office Automation
The exponential
increase in business
expectations is
unsustainable for IT
MILLIONS OF
APPS, BILLIONS
OF USERS AND MACHINES
* Source: Gartner, 2013: “Hunting and Harvesting in a Digital World: The 2013 CIO Agenda”
$ $ $ $ $ $
ABILITY OF IT TO
DELIVER S/W
STEADY IT
BUDGETS*
BUSINESS
EXPECTATIONS

“The future is already here-
It is just not very evenly
distributed”.
William Gibson 1993

What Have Consumer Internet Giants Been Able to Do?
Rapid App
Development
MAINFRAME
CLIENT-SERVER&WEB
Store and reason
over very large
amounts of data
Operate and
Automate at Scale
Cloud
Consumer
Internet
Giants
New user experiences
New business models
New business value
APPS
ANALYTICS DATA
Speed!

MAINFRAME
CLIENT-SERVER&WEB
...ETC
Rapid App
Development
Store and reason
over very large
amounts of data
Operate and
Automate with
choice of Cloud
Internet of Things
(devices and
machines)
Interoperate with
Legacy
What about Enterprises?
New user experiences
New business models
New business value
APPS
ANALYTICS DATA
Speed!

EMC Family Response…a New CompanyMAINFRAME
CLIENT-SERVER&WEB

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

Cloud Fabric
Data Fabric Application Fabric
Scale-out storage: HDFS/Object
Languages
&
Frameworks
Ingest & Query: very high-capacity &
in-memory AnalyticsServices
Cloud Abstraction (portability)
Automation: App Provisioning & Life-cycle
Service Registry
Pivotal One: Redefining Enterprise PaaS
vFabricGemFire
...ETC

Pivotal Big Data Suite Architecture
Centralized Management
System monitoring System management
Unified Data Management Tier
Data mgmt.
services
MDM
RDM
Audit and
policy mgmt.
Processing Tier
Workflow Management
Distillation Tier
HDFS storage
Unstructured and structured data
In-memory
MPP database
Unified Sources Flexible Actions
Real-time
ingestion
Micro batch
ingestion
Batch
ingestion
Real-time
insights
Interactive
insights
Batch
insights

GE Power and Water Event Collection and
Analytics Architecture
Williams, Aggour, Interrante, McHugh, Pool, “Building High Velocity and High Volume Big Data
Through Distributed In-Memory Storage & Analytics”, GE Global Research / GE Power & Water
See the public article entitled
“Can in-memory storage solve
on of data’s greatest
challenges” by Sahus
Sreedhard, January 14,2014 at
http://www.industrialinternet.co
m/blog/can-in-memory-
storage-solve-one-of-datas-
greatest-challenges/

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

Network User Behavior Anomaly Detection
Customer
Several major global enterprises
Business Problem
Detect anomalous user behaviors in the
global enterprise computer network
Challenges
10 Billions events in 6 months; 15K+
network devices; No existing SIEM solutions
can model user behavioral resource access
baseline and enable anomaly detection in an
adaptive and scalable architecture.
Solution
An innovative Graph Mining based algorithmic
framework with advanced Machine Learning.
Network topology and temporal behaviors are both
modeled. (Patent pending). Implemented in MPP
and PL/R, enabling parallel model training and
behavior risk scoring. Successfully identified DLP
violating anomalous users.

Rapid Innovation Requires a New Approach
 Dramatically improve developer experience
 Agile teams, rapid iteration
 Ingest and incubate open source
advancements and new data services
 Continuous delivery, no planned downtime
 Instant scaling of apps and data services
 Automation and deployment consistency
at every step
DEVELOPERSOPERATORS

Compelling and Disruptive in the Enterprise
From Idea to Production in a Day with Cloud Foundry
Cloud Foundry's potential to transform business is vast
Actual user operation costs reduced 90% with Cloud Foundry
A platform to innovate & experiment at low cost & build fast fail cycles
The ultimate flexibility: Public, Private, Polyglot. ROI is hard to argue
Agile platform meets Agile development

Companies are Placing Their Bets

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

How to get Started?
• Decide where on the Circle to start
• Everyone is different
• Any starting place is good
• You only need to plan 1 step ahead
• Think small & fast
• Less than 3 month project
• A small app or small data can work
• You don’t even need physical infra
• Co-Innovation Services
• Data Science Labs
• Pivotal Labs
• Pivotal Innovation Centre

2014 Big_Data_Forum_Pivotal

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