Watch the live session on-demand: https://goo.gl/qAL3Q7 No time like the present! That's one reason why edge analytics continues to grow in value and importance. With the right analytic architecture in place, companies can not only identify opportunities at the edge, they can take appropriate actions. Watch this Denodo DataFest 2017 session to discover: • The growing importance of edge computing in IoT • How data virtualization plays a critical role in enabling edge analytics • How Denodo’s innovative customers exploit edge for a winning business model

1. Conquering the Edge with Data Virtualization
Lakshmi Randall
Director of Product Marketing, Denodo
Twitter: @LakshmiLJ

2. 2
Source: Gartner Blog
(http://blogs.gartner.com/thomas_bittman/2017/03/06/the-edge-will-eat-the-
cloud/)
The edge will eat the cloud. And this is
perhaps as important as the cloud
computing trend ever was.”

3. Which workloads will need an edge presence?
The need for an edge presence will be defined & driven
by data requirements …
Speed
Availability
& reliability
of access
Rate of
generation
or security
…or any combination
Source: 451 Research
3

4. Which deployment locations do you plan to use to store and analyze IoT
data in 2017
Source: 451 Research, Voice of the Enterprise: IoT Budgets and Outlook, 2017. n = 288
4

5. 5
IoT Network Architecture
• IoT networks will contain multiple ‘layers’ of compute systems
• Each layer will persist data
• Some storage (e.g. at the sensor) might be temporary
• e.g. last hour of readings
• Not all data can be transmitted to next layer
• Regulatory, privacy, security reasons leaving data where it is
• Data moving to higher levels is typically aggregated for analytics
and monitoring

6. 6
Layer 1 Layer 2 Layer 3 Layer 4
Layered Architecture

7. 7
Data Virtualization: Five Essential Capabilities
4. Self-service data
services
5. Centralized metadata,
security & governance
1. Data abstraction
2. Zero replication, zero
relocation
3. Real-time information

8. 8
• Data Virtualization provides data
access at the edge layer and
data center/cloud layer
• Connected DV platforms
optimize data access by pushing
processing to the data
• Data Virtualization can be
deployed at any intermediary
layers between ‘edge’ and data
center/cloud layer
• e.g. Organizational, Regional, or
Country based layers
Data Virtualization at the Edge

9. 9
Connect rather than Collect Paradigm
Case Study Example from Gartner
Single Logical
View

10. 10
DV Layer @ Edge
DV Layer @ Data
Center/Cloud
Layer 1 Layer 2 Layer 3 Layer 4
Layered Architecture

11. Business Need Solution Benefits
11
Leading Heavy Equipment Manufacturer Improves Service
Delivery and Revenue
• Competitive pressure from low-cost
Chinese manufacturers
• Needed a proactive approach to customer
service to differentiate
• Sought to improve equipment and
services delivery through predictive
maintenance
• Phased rollout systematically improved
asset performance and proactive
maintenance
• Increased revenue from sale of services
and parts
• Reduced warranty costs of parts failure
• Future – optimize pricing for services and
parts among global service providers
• Telemetry (IoT) data from sensors
embedded in the equipment is stored in
Hadoop to perform predictive analytics
• Denodo integrates analytics data with
parts, maintenance, and dealer
information stored in traditional systems
• It then feeds the predictive maintenance
information to a customer dashboard
In business for over 90 years and is the world’s leading manufacturer of construction and mining
equipment, diesel and natural gas engines, industrial gas turbines and diesel-electric locomotives
with 2014 net revenue of $54.6 billion.
Denodo @ Layer 4 (Data Center/Cloud)

12. 12
Denodo @ Layer 4 (Data Center/Cloud)
Telemetry Analytics

13. Business Need Solution Benefits
13
World’s Largest Independent Biotechnology Firm
Successfully Increases Production Yield and Plant Efficiency
• Manufacturing data had to be aligned in
real time to identify “weak signals” that
could hinder production and cost millions
• Streaming data from manufacturing,
laboratory facilities, etc. had to be
analyzed for optimal plant efficiency
• “Golden Batch” had to be defined for
increased production yield after
combining current/historical data
• Improving yield by 1–3% has saved $10 –
30 million per plant
• Concerns with operating a new plant at
low capacity are eliminated
• ROI of Denodo easily justifies all work –
will be used for all future applications
• Data Virtualization through Denodo is
cheaper and more efficient than ETL
• Denodo Platform combines current
manufacturing process data with historical
data and feeds it to analytical systems
• The systems analyze virtualized data,
determine “golden batch,” and take
corrective steps to ensure ideal yield
• Denodo combines data from 46 source
systems, of which 12 are key systems
Headquartered in Thousand Oaks, California, the company is the world’s largest independent
biotechnology firm, and sells a number of products treating autoimmune diseases, rheumatoid
arthritis, etc. In 2015, the company reported a revenue of $20 billion with its 18,000 employees.
Denodo @ Layer 3 (at the Edge)

14. 14
Denodo Beyond the Edge
• Data Virtualization can also be leveraged beyond the edge – at
local data collection level
• e.g. factory or production line, retail store, ICU in hospital, etc.
• Collection point for real-time data streams from sensors, devices
• Stored in local databases, files, etc.
• For sensor management and control
• DV provides data access for analytics and reporting
• Lightweight deployment for lower capability systems
• e.g. Denodo in Docker Container

15. 15
DV Layer @ Edge
DV Layer @ Data
Center/Cloud
Lightweight DV Layer
Beyond Edge
Layer 1 Layer 2 Layer 3 Layer 4
Layered Architecture

16. Business Need Solution Benefits
16
Large US Telecom Company Improves Inventory Alignment with
Virtualized Master Data Views
• Business users needed single view of
networking device, customer, and site
information from systems
• The single views were needed for ticket
and event enrichment, compliance and
audit, and inventory alignment
• The information was dispersed across
multiple systems with varying levels of
completeness and data quality
• Customer satisfaction: High quality MDM
data helped end customer receive better
quality information
• Inventory alignment: Improved
information availability to internal and
external applications
• Improved compliance: Better inventory
management and analytics helps with
better compliance and audit standards
• Denodo is used as a virtualized master
data management (MDM) system
• Denodo connects to multiple systems and
Hadoop
• Denodo aggregates and exposes the
master data views via an integrated API
layer
• Data is consumed by ticket and event
enrichment, dashboard, and compliance /
audit applications
The company is the second largest cable company in the U.S. by revenue, operating in 29 states, with corporate
headquarters located in Midtown Manhattan, New York City. The company recorded revenue of US$23.69 billion in
2015. It’s Network Operations group is a thriving business providing the entire range of telecommunications
services to both individual and commercial consumers.
Denodo Beyond the Edge

17. 17
Denodo and Streaming Data
• Integrate with streaming data
systems e.g. Spark, Kafka, IBM
Streams, etc.
• Read data from queues
or streams (e.g. Spark
data frames) during
ingestion process
• Also enrich streaming
data through Denodo
Platform
• Access temporal data in specific
time window
• e.g. last 2 hours of
readings/data
Read temp
window buffers
SQL based
enrichment
Secure
+
Combine
+
Enrich

18. 18
Complete enterprise information, combining Web, cloud,
streaming, and structured data
ROI realization within 6 months, with the flexibility to
adjust to unforeseen changes
An 80% reduction in integration costs, in terms of
resources and technology
Real-time integration and data access, enabling faster
business decisions
“Get it Real-time and Get it Fast!”
The Benefits of Data Virtualization

19. Thank you!
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