Pivotal Big Data Roadshow

The Journey to Becoming a Data-
Driven Enterprise
Pivotal Big Data Roadshow 2015

2© Copyright 2015 Pivotal. All rights reserved.
Where we’re going today…
3 Great Keynotes
•  Journey to a Data-driven Enterprise
•  Data Science Use Cases
•  Streaming Data and Internet of Things
Internet of Things Demo and Architecture Overview
Intensive hands-on training sessions

Today’s Agenda
8:00 AM - 9:00 AM - Check-In & Breakfast
9:00 AM - 10:15 AM - Journey to a Data-Driven Enterprise…
10:15 AM - 10:30 AM - Coffee Break
10:30 AM - 12:00 PM - Internet of Things Demo and Architecture Overview
12:00 PM - 1:00 PM - Lunch & Birds of a Feather Discussion
1:00 PM - 3:00 PM - Hands-On Technical Workshop

© Copyright 2015 Pivotal. All rights reserved.
MASHING BIG DATA WITH BIG MACHINES
IS ‘BEAUTIFUL, DESIRABLE, INVESTABLE’
- IT COULD TRANSFORM GE'S BUSINESS -
AND THE ECONOMY.
“
”JEFF IMMELT, CEO, GE

ANALYZING INTERNET OF THINGS USING
BIG DATA SUITE
Internet of Things matter for...
•  Industrial Manufacturers
•  Transportation
•  Healthcare, Life Sciences
•  Financial Services
•  Retail
•  Telecom and Media

THE POWER OF 1
R
X
Increasing
Freight Utilization Rail
Predictive
Maintenance Healthcare
Predictive
Diagnostics Power
Driving Outcomes That Matter
One Percent Improvement Equals
$27B
Industry Value by
Reducing System
Inefficiency
$63B
Industry Value by
Reducing Process
Inefficiency
$66B
Industry Value with
Efficiency Improvements
In Gas-fired Power
Plant Fleets
Source: General Electric

THE INTERNET OF THINGS JOURNEY
STORE
•  Structured
•  Unstructured
•  High Volume
•  High Velocity
ANALYZE
•  Predictive Analytics
•  Machine Learning
•  Advance Data Science
•  Realtime Analytics
DEVELOP
•  Advanced Analytic Pipelines
•  Realtime Analytical Applications
•  Global Scale Data-Driven
Applications
•  Enterprise, Consumer, IoT, and
Mobile
INNOVATE
•  Agile Dev Expertise
•  DevOps
•  Hybrid Cloud
•  Continuous Delivery
•  Closed Loop Applications
AGILE DEVELOPMENT
BIG DATA
PREDICTIVE ANALYTICS
ENTERPRISE PAAS

0% of CIOs think
their IT infrastructure
is fully prepared for
big data (3)
30% of
companies have
deployed advanced
analytics, 11% big
data analysis (4)
44% of new applications
failed to meet performance
expectations (5)
2X
90% of companies
allocate at least 2X
more cloud capacity
than needed to ensure
performance (6)
But…
80% of
CEOs thinking
data mining and
analysis are
strategically important
(1)
4% of
companies use
analytics
effectively (2)
(1) 2015 PWC CEO Survey; (2)2013 Baine and Company - The Value of Big Data; (3) 2014 IT Infrastructure Conversation - IBM; (4)
Ernest and Young - 2014 Enterprise IT Trends and Investments; (5) 2014 Riverbed Tecnologies - The Transformers; (6) 2014 ElasticHosts CIO Study
LARGE ENTERPRISE BIG DATA TROUBLE

BIG DATA
CHASM
70%
of data
generated by
customers
80%
of data stored
3%
prepared for
analysis
0.5%
being
analyzed
<0.5%
being
operationalized
9
THE DATA DIVIDE

Software Is Eating The World
Data Is Fueling Software
SOFTWARE IS EATING THE WORLD

WE CHOSE PIVOTAL BECAUSE WE BELIEVE IT
PROVIDES A 360-DEGREE VIEW OF THE PROCESS.
FROM A DATA SCIENCE AND DATA TECHNOLOGY
PERSPECTIVE, IT MEANS DELIVERING BEST-IN-
CLASS DATA TECHNOLOGIES AND ENABLING THEM
ON THEIR PLATFORM.
“
”

ACROSS INDUSTRIES

THE NEW DATA IMPERATIVES
Converged
Data & Cloud
OpenData-Driven
Apps

THE BIG DATA PROBLEM
Fragmentation ConstraintsComplexity

•  Remove Lock-in
•  Leverage Ecosystem
•  Co-innovate
GUIDING PRINCIPLES IN THE NEW ERA
OPEN AGILE CLOUD-READY
•  Shorten innovation
cycles
•  Reduce TCO
•  Improve TTM
•  Solve business
problems
•  Avoid lock-in
•  Appropriate security

JOURNEY TO A DATA-DRIVEN ENTERPRISE
Deploy analytic apps and
automate at scale
Perform advanced analytics
Discover insights
Modernize data
infrastructure

automate at scale
Discover insights
Modernize data
infrastructure
DATA-DRIVEN COMPANIES:
USE MODERN DATA INFRASTRUCTURE

MODERNIZE DATA INFRASTRUCTURE
Elastic, Scale-out
storage and processing
Flexible data types and
pipelining
ETL on demand: low operational cost
Expanded use cases
Higher quality analytics
Lowered storage/processing cost
Less fragmented ecosystem
Reduced vendor lock-in
REQUIREMENTS BENEFITS
Cloud friendly and
open-source based

Modernize data
infrastructure
automate at scale
Discover insights
STRATEGICALLY USE ADVANCED ANALYTICS

ADVANCED ANALYTICS
Leverage existing skills and tools
Rapid time to insights
Internet of Things use cases
Rapid time to insights
Solve business problems
Predictive insights: proactive execution
Machine learning and
advanced analytics
01010101010101
01001010101010
10101100101010
SQL- compliant batch
and interactive queries
Massive stream
processing
0101010101010101001010
1010101010110010101010
10101010

Modernize data
infrastructure
Discover insights
INNOVATE AT SCALE
automate at scale

ANALYTIC APPS AND AUTOMATION AT SCALE
Reduced time to action
Low ‘analytics ó app-dev’ integration cost
Reduced time to insights
Flexible ingestion: low operating cost
High performance: low operating cost
Transactional safety: business critical ops
Low-latency, distributed
in-memory transactions
Resilient, scale-out
messaging and object storage
Agile analytic app-dev
with enterprise PaaSPaaS

JOURNEY TO A DATA-DRIVEN ENTERPRISE
automate at scale
Discover insights
Modernize data
infrastructure
Pivotal Data Science
helps you move from BI to
Data Science
Pivotal Labs
helps you move to an
agile development of apps
at scale
Pivotal Data Engineering
helps you move from data
administration to data engineering

•  Remove Lock-in
•  Leverage Ecosystem
•  Co-innovate
GUIDING PRINCIPLES IN THE NEW ERA
•  Shorten innovation
cycles
•  Reduce TCO
•  Improve TTM
•  Solve business
problems
•  Avoid lock-in
•  Appropriate security

World’s First
Open Sourced,
Enterprise-Class Data
Portfolio
+
Open Data Platform
PIVOTAL BIG DATA SUITE
Modern Data
Infrastructure
+
Advanced Analytics
+
Apps at Scale
Multiple Cloud
Deployment Models
+
Big Data Suite on Pivotal
Cloud Foundry

Open sourcing all Pivotal Big Data Suite components including:
WORLD’S FIRST OPEN SOURCED BIG DATA
PORTFOLIO
BUILDING ON SUCCESS OF CLOUD FOUNDRY FOUNDATION
BUILT FOR ENTERPRISES
Pivotal GemFire Pivotal HAWQPivotal
Greenplum Database

BUILT FOR ENTERPRISES
Value added features: enterprise grade performance + robustness without lock-in
•  Advanced Query Optimization in analytics
•  WAN replication and continuous query in transactional processing
Flexible Deployment models: align to business objectives and needs
•  Balance cost objectives with policy and compliance requirements
•  Leverage Pivotal’s pre-integration + certification on supported configurations
Enterprise grade support: one throat to choke for the suite
•  Focus on business problems – not on lifecycle management
•  Expert support on Big Data Suite means reduced business risk

•  Common core for Hadoop ecosystem
•  Rapidly accelerated certifications, ecosystem
development and enterprise-grade quality
OpenDataPlatform.org
OPEN

AGILE
automate at scale
Discover insights
Modernize data infrastructure
Spring XD Spark Pivotal HD & Open Data Platform
Pivotal Greenplum Database
Pivotal HAWQ Rabbit MQ
Redis
Pivotal GemFire Pivotal BDS on PCF
Pivotal Cloud Foundry

CLOUD-READY
COMMODITY
HARDWARE
APPLIANCE HYBRID CLOUDCLOUD
IaaS IaaS
PAAS

THE INTERNET OF THINGS JOURNEY WITH
STORE
•  Structured
•  Unstructured
•  High Volume
•  High Velocity
ANALYZE
•  Predictive Analytics
•  Advance Data Science
•  Realtime Analytics
DEVELOP
•  Advanced Analytic Pipelines
•  Realtime Analytical Applications
•  Global Scale Data-Driven
Applications
•  Enterprise, Consumer, IoT, and
Mobile
INNOVATE
•  Agile Dev Expertise
•  DevOps
•  Hybrid Cloud
•  Continuous Delivery
•  Closed Loop Applications
AGILE DEVELOPMENT
BIG DATA
PREDICTIVE ANALYTICS
ENTERPRISE PAAS
Spring XD
Spark
Pivotal HD &
Open Data Platform
Spring XD
Pivotal Greenplum
Database
Pivotal HAWQ
Spring XD
Pivotal GemFire
Redis
Rabbit MQ
Spring IO
Groovy
Pivotal BDS on PCF
Pivotal Cloud Foundry
Pivotal LabsData ScienceData Engineering

WHY PIVOTAL FOR BIG DATA ?
Complete
platform
SQL on Hadoop
leadership
Deployment
options
Open source
Flexible licensing
Advanced data
services
Pivotal Data Engineering Pivotal LabsPivotal Data Science

Complete
platform
SQL on Hadoop
leadership
Deployment
options
Open source
Flexible licensing
Advanced data
services
WHY PIVOTAL FOR BIG DATA ?
Pivotal Data Engineering Pivotal LabsPivotal Data Science

FOR FURTHER INFO, CHECKOUT…
•  Pivotal Data Product Info, Docs and Downloads @ http://pivotal.io/big-data
•  Pivotal Blog @ http://blog.pivotal.io
•  Pivotal Data Science Blog @ http://blog.pivotal.io/data-science-pivotal
•  Pivotal Academy @ https://pivotal.biglms.com
Or reach out to your local Pivotal Account Executive…

36© Copyright 2015 Pivotal. All rights reserved. 36© Copyright 2013 Pivotal. All rights reserved.
Pivotal Data
Science Overview
and Use Cases
Pivotal Big Data Roadshow

DATA SCIENCE?
App Development
Analytics
Business Intelligence
Reporting
Visualization
Dashboards
Insights
Big Data
Machine Learning
Statistics
Mathematics
Time Series
Algorithms
Databases
Software
Modeling
Queries
Real-Time
Sensors
Predictive Models
ETL
Research
Hadoop
Distributed Computing
MapReduce
SQL
In-Memory
OLAP
Text Mining
Unstructured Data
Open Source
Decision Science
Ad Hoc Queries
Hacking
In-Database Analytics
Internet of Things
Data Cleansing
Sentiment

•  ETL
•  Unstructured
•  Data Cleansing
•  Sensors
Data Related
•  Algorithms
•  Mathematics
•  Time Series
•  Statistics
•  Predictive Modeling
•  Text Mining
•  Sentiment
•  Map Reduce
Fields of Study &
Techniques
•  Dashboards
•  Insights
•  Visualization
•  Ad Hoc Queries
•  Reporting
Business
Intelligence
•  Software
•  In-Database
Analysis
•  Distributed
Computing
•  Hadoop
•  Open Source
Implementation
•  Big Data
•  Decision
Science
•  Internet of
Things
•  Real-Time
•  Hacking
•  In-Memory
Industry
Buzzwords

What is Data Science?
The use of statistical and machine learning techniques on
big multi-structured data in a distributed computing
environment to identify correlations and causal
relationships, classify and predict events, identify patterns
and anomalies, and infer probabilities, interest, and
sentiment.
DRIVE AUTOMATED, LOW-LATENCY ACTIONS IN RESPONSE TO EVENTS OF INTEREST

Gene Sequencing
Smart Grids
COST TO SEQUENCE
ONE GENOME
HAS FALLEN FROM
$100M IN 2001
TO $10K IN 2011
TO $1K IN 2014
READING SMART METERS
EVERY 15 MINUTES IS
3000X MORE
DATA INTENSIVE
Stock Market
Social Media
FACEBOOK UPLOADS
250 MILLION
PHOTOS EACH DAY
Billions of Data Points
Oil Exploration
Video Surveillance
OIL RIGS GENERATE
25000
DATA POINTS
PER SECOND
Medical Imaging
Mobile Sensors

What is Big Data Analytics?
Descriptive
Analytics
WHAT HAPPENED?
Diagnostic
Analytics
WHY DID IT HAPPEN?
Predictive
Analytics
WHAT WILL HAPPEN?
Prescriptive
Analytics
HOW CAN WE MAKE IT HAPPEN?
Complexity
Value of
Analytics
($)

Pivotal Big Data Suite
P L A T F O R M
Data Science Toolkit
KEY TOOLS KEY LANGUAGES
SQL

A single address for everything analytics
Analytics with Pivotal
Time-to-Insights
FORECASTING CLUSTERING
REGRESSION
CLASSIFICATION
OPTIMIZATION

Smart Systems = Sensors + Digital Brain + Actuators
Problem
Formulation
Modeling
Step
Data Step
Application
Step
Data Science for
Building Models
Sensors &
Actuators
Data Lake

Data Science
Use Cases

Smart Meter
Analytics

The Digital Brain: Making a Smart Grid Smarter!
Action:
Where (and when) to
send trucks, preventive
maintenance
The Digital Brain:
Uses Fourier transform
extracts patterns and
flags outliers/anomalies
Input:
Data from smart
meters

Smart Meter Analytics – Significant Use Cases
•  Load profiling
•  Theft prevention
•  Demand prediction
•  Load forecasting
•  Root cause of power failures
•  Black-out warning
•  Anomaly detection
•  Network topology error
detection

SOLUTION
•  Analyze smart meter power data using
unsupervised clustering techniques and detect
anomalies based on distance metric in clusters
•  Reduce time required to monitor and improve
grid efficiencies
•  Leveraged the MPP architecture of Pivotal
GPDB and MADlib in-database machine
learning library for fast computation at scale
Electricity Network Load Profiling and Outlier
Detection
CUSTOMER
A major smart grid infrastructure provider
BUSINESS PROBLEM
Profile power consumption patterns based on smart
meter data and flag anomalous usage
CHALLENGES
•  Large volume of smart meter data (several
months of data from 100s of thousands of
meters) could not be analyzed effectively by
legacy system
•  Timely business insights on large scale smart grid
infrastructure demand fast processing of data

Electricity Network Load Profiling and Outlier
Detection
Dashboards for navigating clusters and outliers

Network Topology Error Detection
CUSTOMER
A major utility
BUSINESS PROBLEM
Use load and voltage meter readings to determine
errors in transformer network topologies
CHALLENGES
•  Time consuming process to detect network
topology errors on entire network in legacy
system
•  Timely detection of network topology errors
requires big data infrastructure and analytical
capabilities
SOLUTION
•  For each transformer network in parallel, solve
an LP to determine scale of topology error,
which can be used to flag and rank anomalous
network topologies
•  Reduce time for topology error detection from
several days/weeks to few minutes!

Security and
Fraud

Attacker elevates
access to important
user, service and
admin accounts, and
specific systems
Data is acquired from
target servers and
staged for exfiltration
Data is exfiltrated via
encrypted files over
ftp to external,
compromised
machine at a hosting
provider
A handful of users are
targeted by two
phishing attacks: one
user opens Zero day
payload
(CVE-02011-0609)
The user machine is
accessed remotely by
Poison Ivy tool
Advanced Persistent Threat (APT)
APT Kill Chain
1 432
Phishing & Zero
Day Attack
Back Door Lateral Movement Data Gathering Exfiltrate
5

Anomalous User-to-Resource Access Detection
BUSINESS PROBLEM
Detect anomalous user behaviors in the global
enterprise computer network
SUMMARY
Given local-to-local communication data, identify
anomalous users within an enterprise.
Ÿ  Reduce malware-dwell time, typical 243 days
Ÿ  Signature-based approaches cannot detect
such behavior
CHALLENGES
10 Billion 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.

Financial
Services

Identifying and Pricing Cross-Sell Opportunities
CUSTOMER
A global financial services provider
BUSINESS PROBLEM
Identify cross-sell opportunities between two
business arms of a financial institution.
CHALLENGES
Integration of large-scale data originating from
multiple data warehouses. Developing predictive
models to identify novel cross-sell opportunities
within the financial institution. Evaluate the
identified cross-sell opportunities by their revenue
potential.
SOLUTIONS
Ÿ  Fast integration of data in Pivotal Greenplum
Database.
Ÿ  Predictive models and evaluation of profitability:
–  Association rule.
–  Logistic regression for each product
offered.
–  Estimation of revenue opportunity.
Ÿ  On-demand reporting and visualization via
custom dashboards connected to
in-database models.

Credit Risk Assessment and Stress Testing
CUSTOMER
A global financial services provider
BUSINESS PROBLEM
Speed up the process of compliance reporting and
stress testing for Basel III.
CHALLENGES
Running the calculation procedures on the
customer’s legacy database were time-consuming,
therefore had to be done in overnight batch mode.
SOLUTION
Ÿ  Implement risk asset calculation and stress
testing on Pivotal Greenplum Database.
Ÿ  Three years of data was processed in well
under 2 minutes, significantly faster than the
customer’s current procedures.
Ÿ  Connect an “in-
database” visualization
tool to Pivotal Greenplum
Database via ODBC for
on-demand reporting and
visualization.

Financial Compliance
BUSINESS PROBLEM
•  Ensure compliance with Dodd-Frank and Basel
Committee regulations
•  Identify underlying risk and fraud while reducing the
compliance department’s overburdened
Emails Chats Trades
Transactions Policy Securities
Phone Calls Watch Lists …
Financial compliance
Data Lake
Data
integration
Data clean
up
Modeling
Classification
and ranking
Analyst user interfaces
Feedback
Analytics
Analyst feedback
Data integration: e.g., append
trade information with email and
chat communications
Data cleanup: e.g.,
identify newsletters and
spam emails
Modeling:
•  Predictive modeling to flag
messages and trades
•  Graph and cohort analysis
Analyst feedback
Reviewed fraud instances
included in periodic model
refreshes
SOLUTION
Ÿ  A data lake platform coupled with cutting edge data
science techniques
Ÿ  Flexible user interface to promote an adaptive,
continuously learning compliance framework

Pivotal Topic & Sentiment Analysis Engine
External
Tables
PXF
HDFS
Source: http Sink: hdfs
Parallel Parsing
of JSON
(PL/Python)
HAWQ
Nightly Cron Jobs
Topic Analysis
through MADlib pLDA
Unsupervised
Sentiment Analysis
(PL/Python)
D3.js
Spring XD
Twitter Decahose (~55 million tweets/day)

http://blog.pivotal.io/data-science-pivotal
Check out the Pivotal Data Science Blog!

FOR FURTHER INFO…
•  Or reach out to your local Pivotal Account Executive…

Data Streaming and IoT
Using Pivotal Big Data Suite

Converging
Trends
Innovation
New Data New Processes New Insights
The Journey to the Data-Driven Enterprise
Data Science
and Machine
Learning
Big Data
Internet
of Things

HDFS
Data Lake
Ingest Store Analytics
Hard to change
Labor intensive
Inefﬁcient
Coding based
No real-time information
Based on expensive ETL
Migrating from a Reactive, Static and
Constrained Model…

HDFSData Lake
Expert System /
Machine Learning
In-Memory
Real-Time Data
Continuous Learning
Continuous Improvement
Continuous Adapting
Data Stream Pipeline
Multiple Data Sources
Real-Time Processing
Store Everything
To Pro-Active, Self-Improving, Machine
Learning Systems

New York Times Research: http://www.nytimes.com/2014/08/18/technology/for-big-data-scientists-hurdle-to-insights-is-janitor-work.html
“
50-80% OF THE TIME ON DATA
SCIENCE PROJECTS IS SPENT ON
DATA WRANGLING
”

Data Feeds
Stream Processing
Expert Systems
Machine Learning
Historical Data
Business Value
Smart Decisions
Still…
HDFS
Data Lake

Ingest Transform Sink
SpringXD
GemFire
Data Stream Needs an Agile, Scalable and
Fast Solution
HAWQ GPDB
Data
Lake

SpringXD
Distributed
Computing
In-Memory
Real-Time Data
Spring XD Orchestrates and Automates all the
Steps on Data Stream Pipelining
Expert System /
Machine Learning
Extensible
Open-Source
Fault-Tolerant
Horizontally Scalable HAWQ GPDB
Data
Lake

INGEST / SINK PROCESS ANALYZE
•  No coding required
•  Dozens of built-in
connectors
•  Seamless integration with
Kafka, Sqoop
•  Create new connectors
easily using Spring
•  Call Spark, Reactor or
RxJava
•  Built-in configurable filtering,
splitting and transformation
•  Out-of-box configurable jobs
for batch processing
•  Import and invoke PMML
jobs easily
•  Call Python, R, Madlib and
other tools
•  Built-in configurable
counters and gauges
Spring XD
State of the Art Data Pipeline Automation

SpringXD
Distributed
Computing
GemFire Provides Scalable, Low-Latency
Data Access, Storage and Event Processing
Expert System /
Machine Learning
GemFire
Extensible
Open-Source
Fault-Tolerant
Horizontally Scalable HAWQ GPDB
Data
Lake

GemFire
•  In-Memory Enterprise Data Grid
•  Horizontally Scalable, Consistent, Highly
Available
•  Event handling
•  Continuous Queries
•  Enterprise Data Geo Distribution
In-memory Real Time Data

SpringXD
Distributed
Computing
Pivotal Provides SQL Based
Advanced Analytics
Expert System /
Machine Learning
GemFire
Extensible
Open-Source
Fault-Tolerant
Horizontally Scalable
Data
Lake
HAWQ GPDB

HAWQ
•  Massively Parallel Processing
RDBMS on HADOOP
•  ANSI SQL on Hadoop
•  Extremely high performance for
analytics (not like Hive)
•  Stores all data directly on
HDFS
•  Open-Source
Advanced SQL analytics in Hadoop
Combining SQL with Hadoop is key for analytics
SQL remains #1 choice for Data Science

SpringXD
Developers and Data Scientists Can Focus on
the Business Value of Data
GemFire
Extensible
Open-Source
Fault-Tolerant
Data
Lake
HAWQ GPDB

Data Streaming Reference Architecture
Data Feeds Transactional Apps Analytic Apps
Distributed
Computing
Real-Time Data
Expert Systems &
Machine Learning
Advanced
Analytics
HDFSData Lake

Data Streaming Reference Architecture
Data Feeds Transactional Apps Analytic Apps
HDFSData Lake
GemFire HAWQ GPDB
SpringXD

“
SO WE ARE MOVING TO A WORLD WHERE THE
MACHINES WE WORK WITH ARE NOT JUST
INTELLIGENT; THEY ARE BRILLIANT.THEY ARE
SELF-AWARE, THEY ARE PREDICTIVE, REACTIVE
AND SOCIAL. IT'S A WORLD WHERE INFORMATION
ITSELF BECOMES INTELLIGENT AND COMES TO US
AUTOMATICALLY WHEN WE NEED IT WITHOUT
HAVING TO LOOK FOR IT.
”MARCO ANNUNZIATA, GE

The IoT Market Verticals
Diversified Industrial
Manufacturers
Agriculture, Security, Retail
Auto Manufacturers
Media (via Mobile devices)
Urban Infrastructure, Cities
Consumer, Connected Home
etc.
Healthcare, Life Sciences

“
THE MAGIC HAPPENS WHEN YOU MARRY THE
TRADITIONAL ENGINEERING APPROACH WITH THE
DATA SCIENCE ENABLED BY THE DATA LAKE. IT
OPENS UP A WHOLE NEW WORLD OF POSSIBLE
‘WHAT IF’ QUESTIONS.
”DAVE BARTLETT, GE AVIATION

GE Aviation – Big Data & IoT
•  Goal
•  Improve jet engine efficiency and increase service profitability
•  Unable to store & analyze massive amounts of data for analytics
•  Solution
•  LARGE DATA SETS ingested via batch
•  Store 100s TB of engine data in Hadoop (PHD)
•  Open doors for industrial engineers to poke at data (HAWQ)
•  FAST MACHINE LEARNING based algorithms
•  2000x faster, 10x cheaper
•  Customer portals for visibility

“
THE REAL OPPORTUNITY FOR
CHANGE...SURPASSING THE MAGNITUDE OF THE
CONSUMER INTERNET...IS THE INDUSTRIAL
INTERNET, AN OPEN, GLOBAL NETWORK THAT
CONNECTS PEOPLE, DATA AND MACHINES.
”JEFF IMMELT, CEO, GE

GE Energy – Fast Data & IoT
•  Goal
•  Failing gas turbines causing issues with power generation
•  Unable to store & process fire-hose of data
•  Solution
•  HIGH VELOCITY data ingestion from Gas Turbines
•  Store 10 TB of turbine data in memory (GemFire)
•  VERY LOW LATENCY and HIGH SPEED data access
•  “Predictive” Maintenance

“
… YOU USE THOSE DEVICES TO INSPECT CARS AND
KEEP TRACK OF INSPECTION RECORDS. THAT CAN
THEN FLOW INTO ASSET MANAGEMENT SOFTWARE
TO PROVIDE PREDICTIVE ANALYSIS OF WHEN THE
ASSET NEEDS TO BE MAINTAINED ... WHEN YOU
BOIL THAT DOWN TO BUSINESS, IT’S ABOUT
COMPETITIVE ADVANTAGE.
”BRAD HOWELL, LODESTAR LOGISTICS

GE Transportation – Big & Fast Data
•  Goal
•  Help rail companies manage locomotives better
•  Fast data from tracks & Big data from sensors in locomotives
•  Solutions
•  MACHINE LEARNING MODEL built from combined data set (MADLib)
•  REAL TIME SCORING of rail sensor data (Spring XD)
•  REAL-TIME ALERTING of critical events via email & a REAL TIME DASHBOARD (Spring)

“
WE EXPECT THE PRECISION AGRICULTURE SPACE
TO CONTINUE TO GROW QUICKLY AS DATA
BECOMES CHEAPER TO STORE AND EASIER TO
MOVE FROM PLATFORM TO PLATFORM. WE ARE
JUST BEGINNING TO EXPLORE ALL THE VALUE WE
CAN CREATE FOR FARMERS WITH THESE TOOLS.
”BRETT BEGEMANN, MONSANTO

Monsanto – Agriculture & IoT
•  Goal
•  Help farmers maximize crop yields
•  Solution
•  Use of Big Data to collect & store data from farm equipment
•  Combine with climate and other information
•  Build custom apps using an agile app dev platform (PCF)

IoT - Need for a Platform
A Better Customer Experience
Pivotal
Cloud Foundry
Pivotal
Big Data Suite
Using Innovative Data-Driven Apps
On a Integrated Platform

The Connected Car Architecture
INGESTION
JSON / HTTP
STREAM PROCESSING
Spring XDTransformEnrich
DATA LAKE
Pivotal HDSink
ADVANCED ANALYTICS
HAWQ
REAL-TIME DATA INSIGHTS
GemFire
MOBILE SERVICES
MICROSERVICES
Pivotal CF Dashboard Analytics App Simulator
IoT APPS
Rabbit MQ
PUSH

Fault Tolerant
Extensible
Open-Source
STREAM PROCESSING
Spring XD
Rabbit MQ
DATA LAKE
Pivotal HD
ADVANCED ANALYTICS
HAWQ
ENRICHER PREDICTIVE ANALYTICS
+ Timestamp
& GUID
+ MPG, rangE
& route
MOBILE
APP
JSON
REAL-TIME DATA INSIGHTS
GemFire
CAR
SENSOR
Sink
Tap
DASHBOARD

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