This webinar with Chris Selland of HPE Vertica and Dennis Duckworth of VoltDB addresses the growing challenges with managing a complex IoT solution and how to enable real-time operational interaction with comprehensive data analytics.

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VOLTDB AND HPE VERTICA:
BUILDING AN IOT ARCHITECTURE
FOR FAST + BIG DATA

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OUR SPEAKERS
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Chris Selland
VP Business Development
Hewlett Packard Enterprise
Dennis Duckworth, Director
Product Marketing
VoltDB

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AGENDA
• Introduction
• What is the Internet of Things?
• HPE Vertica and IoT
• VoltDB and IoT
• Demo of VoltDB and HPE Vertica
• Q&A
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4. Building an IoT
Architecture for Big &
Fast Data
Chris Selland
VP Business Development

5. What is the Internet of Things?
“The Internet of Things is the network of physical objects that
contains embedded technology to communicate and sense or
interact with the objects' internal state or the external
environment.”1
Consumer “things”
Personal sensing with remote
monitoring and control
1 Gartner. IT Glossary – Internet of
Things.
Industrial “things”
From individual sensors to
entire power plants

6. Business outcomes
through big data analytics
How can value be created from IoT data?
Up to $11 trillion per year in potential economic impact by 20251
• Increased revenue
• Optimized operations
• New products and services
• Workforce productivity
• Reduced risks
• Reduced operating costs
• Optimized maintenance
• Optimized network throughput
Smart citiesManufacturing
Consumer
Smart homes
Healthcare
Oil & gas
Aviation
Energy & utilities
Transportation
Retail
Industries
that can benefit
Insurance
Agriculture
1 McKinsey Global Institute. The Internet of Things: Mapping the Value Beyond the Hype, June 2015.
Connected devices
source of contextual data

7. IoT is not the future. It’s already here.
Business Outcomes
• Understand driver behaviors and
patterns
• To improve efficiency of the
company and drivers
Business Outcomes
• Safety and well-being of citizens
• Analyzing safety threats
• Accurate identification and scene
analysis
Business Outcomes
• Increases customer value with
equipment monitoring, event
mitigation, & energy performance
services
• Helps customers reduce energy
& maintenance costs
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Equipment Manufacturers Enterprises Service-based Businesses
Integrated sensor and telemetry
analytics in HP Fleet vehicles
Monitors 2000+ cameras and
performs video analytics
Analytics enhance the Trane
Intelligent Services business
Successes in IoT

8. HPE Vertica and IoT
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9. Computing at the Edge – “shifting left” for strategic advantage
Goal
– Processing streaming data as close
to the sensor as possible creates
new opportunities
Advantage
– Processing data streams in real time,
before the data is stored for additional
analysis, creates advantages
– Example: Transformer or turbine thermal
runaway, requires immediate action
Result
– Fast action prior to data storage
prevents data obsolescence and lost
opportunities/alerts
HPE Confidential | Share under NDA 9
“Things” generate data and
need control
Edge IT, data center and cloudOperations technology
Early analytics
and compute
Deep analytics
and compute
Data is sensed,
Things controlled
Data acquired
and aggregated
The Edge
Opportunity
Accelerate insight by moving compute
from the data center to the Edge
Data flow
Control flow
Augment the data center
with processing at the edge

10. The Vertica Real-Time Analytics Engine
Native
High
Availability
Standard
SQL
Interface
Column
Orientation
Auto
Database
Design
Advanced
Compression
MPP
Massive
Parallel
Processing
Leverages BI, ETL,
Hadoop/MapReduce
and OLTP investments
No disk I/O bottleneck
simultaneously load &
query
Native DB-aware
clustering on low-cost
x86 Linux nodes
Built-in redundancy
that also speeds up
queries
Automatic setup,
optimization, and DB
management
Up to 90% space
reduction using 10+
algorithms
ü 50x – 1000x faster
than traditional
RDBMS
ü Scales from TB to
PB with industry-
standard hardware
ü Simple integration
with existing ETL
and BI solutions
ü SQL-99+ compliant
ü Ultimate deployment
flexibility
ü Extended advanced
analytics
ü 24/7 Load & Query
Confidential 10

11. Taking IoT analytics to the next level – Integrated Machine Learning
Native Machine Learning algorithms run in
database (Vertica)
• K-means (anomalies)
• Linear Regression (risks, trends)
• Logistic Regression
Train predictive analytics models in the
datacenter, and easily run at the edge
Utilizes the same hardware as the
database itself
• Lower cost solution
• No transfer of data required
• Large data sets lead to more accurate
models
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12. Taking IoT analytics to the next level – High Performance Messaging
Vertica Streaming Adapter for Apache
Kafka
Kafka replaces custom data ingest
solutions with a robust open-source
implementation
Enables distributed data pipelines for
high throughput and low latency ingest
into Vertica
• Time to insight reduced from hours to
seconds (micro-batch loading)
• Easy handling of data bursts (10m
messages per minute)
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13. Taking IoT analytics to the next level – In-database Sensor Data Analytics
Powerful time series and window
functions (gap filling, interpolation) for
data quality management at the edge1
Live Aggregate Projections
(personalized billing on demand)
Log text analytics and pattern
matching (SIEM – security, intrusion
detection)
Geospatial analytics (location based
services, asset management)
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1 HPE internal testing of Vertica on EL4000 shows it can:
- Repair 5.7 million readings per second
- Load, repair and analyze a reading every 282 nanoseconds

14. www.hpe.com/events/bdc
Big Data Conference 2016
Boston August 29- September 1

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BUILDING AN IOT ARCHITECTURE
FOR FAST + BIG DATA
DENNIS DUCKWORTH
DIRECTOR OF PRODUCT MARKETING

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VOLTDB DOESN’T MAKE THE APPS, WE MAKE THE APPS…
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• Real-time intelligence and context for richer interactions
• Allows unique decisions on each individual event or person
• Analyze and act on real-time/streaming data
• More efficient use of hardware
• 100X faster than traditional transactional databases
• World record performance in the cloud (YCSB)
• Millisecond response times
• Hundreds of thousands to millions of transactions per
second
• High-speed data ingestion
• Simpler apps, easier to test and maintain
• Easier to program with SQL + Java
• Seamless ecosystem integration
• Data is always consistent and correct, never lost
Smarter
Faster
Simpler
1/10
of the Resources Needed
100X
Traditional Transactional DB
100%
Consistent, Correct

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Low Complexity
Rich, Smart
Value of Individual Data Item Value of Data Collection
DataValue
Data Warehouse
Hadoop, etc.NoSQL
Data in Motion Data at Rest
Fast Data Big Data
Feeds, Collectors
CEP
CEP + DB
DataInteraction
FAST DATA + BIG DATA

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Streaming
Analytics
- Filtering
- Windowing
- Aggregation
- Enrichment
- Correlations
Deep
Analytics
- Machine Learning
- Statistical correlations
- Multi-dimensional analysis
- Predictive/Prescriptive analytics
Operational
Interactions /
Transactions
- Context-aware
- Personal
- Real-time
+
Big Data
FAST DATA + BIG DATA
Fast Data

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CUSTOMERS USING VOLTDB FOR IOT TODAY
• Smart Meters (over 60 million meters under
VoltDB management)
• Smart Energy GB
• Smart Metering in Japan (Shikoku and Hokkaido)
• Wearables
• Home Security
• Equipment Service Automation

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EQUIPMENT SERVICE AUTOMATION:
LEADING STORAGE EQUIPMENT VENDOR USING IOT

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#Simplify

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Proven
ü Jepsen Tested
Simpler
ü Fewer components and clusters needed
ü Easier to write, test and maintain applications
Familiar
ü SQL and Java
Glue
Code
Glue
Code
Zookeeper
“DIY” STREAMING DATA INFRASTRUCTURE VS VOLTDB

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EXAMPLE: LARGE TELCO IOT PLATFORM
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Challenges:
• High volume and velocity of data from smart
devices
• Complexity (multiple ingest points, apps,
databases)
• Performance – need to automate action on
inbound data at the velocity of the feeds
Notes
• Applications have their own database
• Each database replicates to Cassandra
and Hadoop
• In-memory grid used to maintain logic and
publish ‘state’ back and forth
• Rules engine for intra-day data to trigger
actions (e.g., ‘turn lights on’)
• PostgreSQL used for dimension data

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BEFORE
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AFTER
Results
ü Simplified system architecture
ü Single ingest point for high-velocity feeds of inbound data
ü Faster time to value

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BEFORE
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OR POSSIBLY
Results
ü Simplified system architecture
ü Single ingest point for high-velocity feeds of inbound data
ü Faster time to value

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DEMO
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DEMO
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QUESTIONS?
• Use the chat window to type in your questions
• Download our ebook,
Architecting for the Internet of Things
(learn.voltdb.com/EbookIoT.html)
• Try VoltDB yourself
Free trial of the Enterprise Edition:
www.voltdb.com/Download
• Email us at: info@voltdb.com
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