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.
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
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