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Grab some
coffee and
enjoy the
pre-show
banter before
the top of the
hour!
The Briefing Room
A Connected Data Landscape: Virtualization and the Internet of Things
Twitter Tag: #briefr The Briefing Room
Welcome
Host:
Eric Kavanagh
eric.kavanagh@bloorgroup.com
@eric_kavanagh
Twitter Tag: #briefr The Briefing Room
  Reveal the essential characteristics of enterprise
software, good and bad
  Provide a forum for detailed analysis of today s innovative
technologies
  Give vendors a chance to explain their product to savvy
analysts
  Allow audience members to pose serious questions... and
get answers!
Mission
Twitter Tag: #briefr The Briefing Room
Topics
March: BI/ANALYTICS
April: BIG DATA
May: CLOUD
Twitter Tag: #briefr The Briefing Room
An Inflection Point for Data
RETHINK your architecture
RECAST your opportunities
REDEFINE your business
Twitter Tag: #briefr The Briefing Room
Analyst: Robin Bloor
Robin Bloor is
Chief Analyst at
The Bloor Group
robin.bloor@bloorgroup.com
@robinbloor
Twitter Tag: #briefr The Briefing Room
Cisco
  Cisco Systems is a known leader in the design,
manufacturing and sales of networking equipment
  Through its acquisition of Composite Software, Cisco has
expanded its footprint in the data virtualization space
  Cisco now offers infrastructure solutions to manage and
analyze streaming data
Twitter Tag: #briefr The Briefing Room
Guest: David Besemer
David Besemer is the Chief Technology Officer of
the Data Virtualization Business Unit (formerly
Composite Software) at Cisco. David works
directly with customers to guide their data
virtualization strategies as well as Cisco's
technology vision and roadmap. David joined
Composite as VP of Engineering in 2002, and
became the CTO in 2006. Before Composite he
was a venture capital CTO in residence, headed
software product marketing at NeXT Computer,
built program trading systems on Wall Street, and
researched natural language processing systems
at GE’s Corporate R&D center. David holds a BS in
Computer Science from Michigan State University
and an MS in Computer Science from Rensselaer
Polytechnic Institute.
The Connected Data Landscape
David Besemer
CTO
Data Virtualization Business Unit
March 3, 2015
©	
  2015	
  	
  Cisco	
  and/or	
  its	
  affiliates.	
  All	
  rights	
  reserved.	
  	
  	
  Cisco	
  Confiden;al	
  
Business Opportunity:
As Data Grows, Leading Businesses Use it to Drive Better Outcomes
•  Customer Profitability
•  Faster Time to Market
•  Cost Reduction
•  Risk Management
•  Compliance
•  Overall Agility
Other
Businesses
Business
Leaders
BusinessOutcomes
Data
Business Outcomes
©	
  2015	
  	
  Cisco	
  and/or	
  its	
  affiliates.	
  All	
  rights	
  reserved.	
  	
  	
  Cisco	
  Confiden;al	
  
Data Silos Proliferating: Data is Now Distributed Everywhere
	
  	
  	
   Cloud Data
Sources
Big Data / IoT
Sources
Traditional
Data Sources
How Does the Business Leverage All the Data?
©	
  2015	
  	
  Cisco	
  and/or	
  its	
  affiliates.	
  All	
  rights	
  reserved.	
  	
  	
  Cisco	
  Confiden;al	
  
Widely	
  Distributed,	
  Streaming,	
  Short	
  Shelf	
  Life,	
  Too	
  Big	
  to	
  Consolidate	
  
“Most data will be
processed at the edge”
(mobile devices, appliances, routers)
Digital Enterprises See an Explosion of Data at the Edge
1230 respondents
Source: Cisco	
  Consul;ng	
  Services	
  Global	
  IoT	
  Study,	
  2014
37%	
  
©	
  2015	
  	
  Cisco	
  and/or	
  its	
  affiliates.	
  All	
  rights	
  reserved.	
  	
  	
  Cisco	
  Confiden;al	
  
Analytics 1.0 Analytics 2.0
Historically, Data has Been Moved, then Analyzed
Traditional Data
Warehouse
Traditional Data
Warehouse
Structured
Data
Unstructured
Data
Structured
Data
Big Data
Store
DV
Hours/Minutes/SecondsDays/Hours
©	
  2015	
  	
  Cisco	
  and/or	
  its	
  affiliates.	
  All	
  rights	
  reserved.	
  	
  	
  Cisco	
  Confiden;al	
  
The Key is Combining Data at the Edge with Data You Store
Data You Store
Big Data
Store
DV
Traditional Data
Warehouse
Data At
The Edge
©	
  2015	
  	
  Cisco	
  and/or	
  its	
  affiliates.	
  All	
  rights	
  reserved.	
  	
  	
  Cisco	
  Confiden;al	
  
Most Valuable
Insight
The Key is Combining Data at the Edge with Data You Store
©	
  2015	
  	
  Cisco	
  and/or	
  its	
  affiliates.	
  All	
  rights	
  reserved.	
  	
  	
  Cisco	
  Confiden;al	
  
12.5 Billion
25 Billion
50 Billion
2015	
   2020	
  2010	
  
Explosion of IoT Connected Devices
©	
  2015	
  	
  Cisco	
  and/or	
  its	
  affiliates.	
  All	
  rights	
  reserved.	
  	
  	
  Cisco	
  Confiden;al	
  
IoT World Forum Reference Model
Levels
Application
(Reporting, Analytics, Control)
Data Abstraction
(Aggregation & Access)
Data Accumulation
(Storage)
Edge Computing
(Data Element Analysis & Transformation)
Connectivity
(Communication & Processing Units)
Physical Devices & Controllers
(The “Things” in IoT)
Collaboration & Processes
(Involving People & Business Processes)
1	
  
2	
  
3	
  
4	
  
5	
  
6	
  
7	
  
Sensors, Devices, Machines,
Intelligent Edge Nodes of all types
Center	
  
Edge
Data	
  at	
  	
  
Rest	
  
Data	
  in	
  	
  
Mo;on	
  
©	
  2015	
  	
  Cisco	
  and/or	
  its	
  affiliates.	
  All	
  rights	
  reserved.	
  	
  	
  Cisco	
  Confiden;al	
  
1	
  
2	
  
3	
  
4	
  
5	
  
6	
  
7	
  
Sensors, Devices, Machines,
Intelligent Edge Nodes of all types
Center	
  
Edge
Levels	
  
IT
	
  
OT
Query
Based
Event
Based
Data at
Rest
Data in
Motion
Non-real
Time
Real
Time
IoT World Forum Reference Model
©	
  2015	
  	
  Cisco	
  and/or	
  its	
  affiliates.	
  All	
  rights	
  reserved.	
  	
  	
  Cisco	
  Confiden;al	
  
Operational ConsistencyData Mobility Optimized Form Factors
UCS Mini
UCS
Mini
UCS for
Enterprise
UCS for Hadoop
Nexus Family
ISR
APIC EM
APMS
CGR
IE
Video
Cloud Services
and Applications
Partner Clouds
Intercloud Core Data Center
Cisco Delivers the Connected Infrastructure You Need
to Reach from the Data Center to the Edge
Fog and Edge
©	
  2015	
  	
  Cisco	
  and/or	
  its	
  affiliates.	
  All	
  rights	
  reserved.	
  	
  	
  Cisco	
  Confiden;al	
  
Levels
Application
(Reporting, Analytics, Control)
Data Abstraction
(Aggregation & Access)
Data Accumulation
(Storage)
Edge Computing
(Data Element Analysis & Transformation)
Connectivity
(Communication & Processing Units)
Physical Devices & Controllers
(The “Things” in IoT)
Collaboration & Processes
(Involving People & Business Processes)
1	
  
2	
  
3	
  
4	
  
5	
  
6	
  
7	
  
Sensors, Devices, Machines,
Intelligent Edge Nodes of all types
Center	
  
Edge
Data	
  at	
  	
  
Rest	
  
Data	
  in	
  	
  
Mo;on	
  
IoT World Forum Reference Model
©	
  2015	
  	
  Cisco	
  and/or	
  its	
  affiliates.	
  All	
  rights	
  reserved.	
  	
  	
  Cisco	
  Confiden;al	
  
•  Mine (fetch)
•  Analyze
•  ReportUsage Data
!
Generate an
Actionable
Event
that is sent to the
Policy System,
Management
System, etc. to
allow immediate
control
Next Generation Analytics
Applies predicates, aggregations, and joins
with metadata tables and contextual data to
identify and match trends.
Querybase Waiting for Data
Store raw data or filtered data for
further mining.
Database Waiting for Queries
Store raw data for further mining.
Traditional Analytics Model
Store first, and query later.
Usage Data
•  Mine (fetch)
•  Analyze
•  Report
Connected Streaming Analytics
©	
  2015	
  	
  Cisco	
  and/or	
  its	
  affiliates.	
  All	
  rights	
  reserved.	
  	
  	
  Cisco	
  Confiden;al	
  
Make your data work for you
Make it actionable in real time
Make it scale without sacrificing latency
Integrate advanced predictive analytics and machine learning
Transparently combine both live and historic data
Value of Real-Time Connected Streaming Analytics
©	
  2015	
  	
  Cisco	
  and/or	
  its	
  affiliates.	
  All	
  rights	
  reserved.	
  	
  	
  Cisco	
  Confiden;al	
  
	
  	
  	
  	
  	
  	
   	
  	
  	
  
Cloud Data SourcesBig Data / IOE SourcesTraditional Data Sources
AnalyticsBusiness Intelligence
Cisco Data Virtualization
Abstrac;on	
   Caching	
   Directory	
  Federa;on	
   Security	
   Governance	
  Transforma;on	
  
Cisco Data Virtualization
©	
  2015	
  	
  Cisco	
  and/or	
  its	
  affiliates.	
  All	
  rights	
  reserved.	
  	
  	
  Cisco	
  Confiden;al	
  
On-demand
Access
Easier and
Faster
Up to 75%
Cost Savings
Cisco Data Virtualization
More AgileHigher Impact Less Expensive
Cisco Data Virtualization
Better Business Outcomes, Faster, for Less
©	
  2015	
  	
  Cisco	
  and/or	
  its	
  affiliates.	
  All	
  rights	
  reserved.	
  	
  	
  Cisco	
  Confiden;al	
  
Analytics 3.0 Seconds/Milliseconds
Traditional Data
Warehouse
Big Data
Store
DV
Real-time/
Streaming
−  Cloud
−  Data Center
−  Fog and Edge
Connecting Distributed Data from the Data Center to the Edge
Analytics 3.0 Requires a New Approach
Twitter Tag: #briefr The Briefing Room
Perceptions & Questions
Analyst:
Robin Bloor
Then the Data
Lake evaporated
into the Cloud
Moving
Stuff
Robin Bloor, PhD
The Architecture of Motion
Move the DATA to the processing
OR
Move the PROCESSING to the data
OR
Move the processing AND the data
OR
Shard and move
The Global Picture
u  IoT (embedded)
u  IoT depots
u  Wearables
u  Mobile devices
u  Web sites (depots)
u  Desktops
u  Data centers
u  Cloud (depots)
u  The network(s)
All can be data creators, data
stores and processing points.
All should be state machines.
The Target(s)
These generalized targets are probably
universal
u  The necessary or best
response time
u  Appropriate availability
up to full fault tolerance
u  Portability - distribution
u  Affordable cost of
operation (for the
benefit delivered)
Distributed Processing
u  The mechanisms for this are
caching and virtualization
u  Sharding involves the caching
or virtualization of specific
fragments (imagine virtualizing
all or part of Hadoop)
u  The management of this
requires the software to be
infrastructure-aware
u  Service levels need to be
specifically defined
u  It is made even more complex
by the reality that all these
resources are shared
Network-Aware Applications
Ultimately we will have
INFRASTRUCTURE-AWARE
software that distributes data and
applications
u  I agree with the Cisco vision. But where has Cisco
applied this thus far? What use cases can you tell
us about?
u  Traditionally Cisco is hardware and networking
infrastructure. Is the company going soft? If so, is
this just for the Big Data business?
u  What are the security components that Cisco
brings to the game?
u  Global directory?
u  Which vendors are you actively partnering with
to deliver this vision?
u  How easy is this? Can you discuss the nature of
a real-world deployment of these capabilities?
u  Is the IoT reference model a blueprint for all
distributed infrastructure and supporting
software?
Twitter Tag: #briefr The Briefing Room
Twitter Tag: #briefr The Briefing Room
Upcoming Topics
www.insideanalysis.com
March: BI/ANALYTICS
April: BIG DATA
May: CLOUD
Twitter Tag: #briefr The Briefing Room
THANK YOU
for your
ATTENTION!
Some images provided courtesy of
Wikimedia Commons and Wikipedia, including:
"Castello Fénis" by Rollopack - Own work. Licensed under CC BY-SA 3.0 via Wikimedia Commons - http://
commons.wikimedia.org/wiki/File:Castello_F%C3%A9nis.jpg#mediaviewer/File:Castello_F%C3%A9nis.jpg

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A Connected Data Landscape: Virtualization and the Internet of Things

  • 1. Grab some coffee and enjoy the pre-show banter before the top of the hour!
  • 2. The Briefing Room A Connected Data Landscape: Virtualization and the Internet of Things
  • 3. Twitter Tag: #briefr The Briefing Room Welcome Host: Eric Kavanagh eric.kavanagh@bloorgroup.com @eric_kavanagh
  • 4. Twitter Tag: #briefr The Briefing Room   Reveal the essential characteristics of enterprise software, good and bad   Provide a forum for detailed analysis of today s innovative technologies   Give vendors a chance to explain their product to savvy analysts   Allow audience members to pose serious questions... and get answers! Mission
  • 5. Twitter Tag: #briefr The Briefing Room Topics March: BI/ANALYTICS April: BIG DATA May: CLOUD
  • 6. Twitter Tag: #briefr The Briefing Room An Inflection Point for Data RETHINK your architecture RECAST your opportunities REDEFINE your business
  • 7. Twitter Tag: #briefr The Briefing Room Analyst: Robin Bloor Robin Bloor is Chief Analyst at The Bloor Group robin.bloor@bloorgroup.com @robinbloor
  • 8. Twitter Tag: #briefr The Briefing Room Cisco   Cisco Systems is a known leader in the design, manufacturing and sales of networking equipment   Through its acquisition of Composite Software, Cisco has expanded its footprint in the data virtualization space   Cisco now offers infrastructure solutions to manage and analyze streaming data
  • 9. Twitter Tag: #briefr The Briefing Room Guest: David Besemer David Besemer is the Chief Technology Officer of the Data Virtualization Business Unit (formerly Composite Software) at Cisco. David works directly with customers to guide their data virtualization strategies as well as Cisco's technology vision and roadmap. David joined Composite as VP of Engineering in 2002, and became the CTO in 2006. Before Composite he was a venture capital CTO in residence, headed software product marketing at NeXT Computer, built program trading systems on Wall Street, and researched natural language processing systems at GE’s Corporate R&D center. David holds a BS in Computer Science from Michigan State University and an MS in Computer Science from Rensselaer Polytechnic Institute.
  • 10. The Connected Data Landscape David Besemer CTO Data Virtualization Business Unit March 3, 2015
  • 11. ©  2015    Cisco  and/or  its  affiliates.  All  rights  reserved.      Cisco  Confiden;al   Business Opportunity: As Data Grows, Leading Businesses Use it to Drive Better Outcomes •  Customer Profitability •  Faster Time to Market •  Cost Reduction •  Risk Management •  Compliance •  Overall Agility Other Businesses Business Leaders BusinessOutcomes Data Business Outcomes
  • 12. ©  2015    Cisco  and/or  its  affiliates.  All  rights  reserved.      Cisco  Confiden;al   Data Silos Proliferating: Data is Now Distributed Everywhere       Cloud Data Sources Big Data / IoT Sources Traditional Data Sources How Does the Business Leverage All the Data?
  • 13. ©  2015    Cisco  and/or  its  affiliates.  All  rights  reserved.      Cisco  Confiden;al   Widely  Distributed,  Streaming,  Short  Shelf  Life,  Too  Big  to  Consolidate   “Most data will be processed at the edge” (mobile devices, appliances, routers) Digital Enterprises See an Explosion of Data at the Edge 1230 respondents Source: Cisco  Consul;ng  Services  Global  IoT  Study,  2014 37%  
  • 14. ©  2015    Cisco  and/or  its  affiliates.  All  rights  reserved.      Cisco  Confiden;al   Analytics 1.0 Analytics 2.0 Historically, Data has Been Moved, then Analyzed Traditional Data Warehouse Traditional Data Warehouse Structured Data Unstructured Data Structured Data Big Data Store DV Hours/Minutes/SecondsDays/Hours
  • 15. ©  2015    Cisco  and/or  its  affiliates.  All  rights  reserved.      Cisco  Confiden;al   The Key is Combining Data at the Edge with Data You Store Data You Store Big Data Store DV Traditional Data Warehouse Data At The Edge
  • 16. ©  2015    Cisco  and/or  its  affiliates.  All  rights  reserved.      Cisco  Confiden;al   Most Valuable Insight The Key is Combining Data at the Edge with Data You Store
  • 17. ©  2015    Cisco  and/or  its  affiliates.  All  rights  reserved.      Cisco  Confiden;al   12.5 Billion 25 Billion 50 Billion 2015   2020  2010   Explosion of IoT Connected Devices
  • 18. ©  2015    Cisco  and/or  its  affiliates.  All  rights  reserved.      Cisco  Confiden;al   IoT World Forum Reference Model Levels Application (Reporting, Analytics, Control) Data Abstraction (Aggregation & Access) Data Accumulation (Storage) Edge Computing (Data Element Analysis & Transformation) Connectivity (Communication & Processing Units) Physical Devices & Controllers (The “Things” in IoT) Collaboration & Processes (Involving People & Business Processes) 1   2   3   4   5   6   7   Sensors, Devices, Machines, Intelligent Edge Nodes of all types Center   Edge Data  at     Rest   Data  in     Mo;on  
  • 19. ©  2015    Cisco  and/or  its  affiliates.  All  rights  reserved.      Cisco  Confiden;al   1   2   3   4   5   6   7   Sensors, Devices, Machines, Intelligent Edge Nodes of all types Center   Edge Levels   IT   OT Query Based Event Based Data at Rest Data in Motion Non-real Time Real Time IoT World Forum Reference Model
  • 20. ©  2015    Cisco  and/or  its  affiliates.  All  rights  reserved.      Cisco  Confiden;al   Operational ConsistencyData Mobility Optimized Form Factors UCS Mini UCS Mini UCS for Enterprise UCS for Hadoop Nexus Family ISR APIC EM APMS CGR IE Video Cloud Services and Applications Partner Clouds Intercloud Core Data Center Cisco Delivers the Connected Infrastructure You Need to Reach from the Data Center to the Edge Fog and Edge
  • 21. ©  2015    Cisco  and/or  its  affiliates.  All  rights  reserved.      Cisco  Confiden;al   Levels Application (Reporting, Analytics, Control) Data Abstraction (Aggregation & Access) Data Accumulation (Storage) Edge Computing (Data Element Analysis & Transformation) Connectivity (Communication & Processing Units) Physical Devices & Controllers (The “Things” in IoT) Collaboration & Processes (Involving People & Business Processes) 1   2   3   4   5   6   7   Sensors, Devices, Machines, Intelligent Edge Nodes of all types Center   Edge Data  at     Rest   Data  in     Mo;on   IoT World Forum Reference Model
  • 22. ©  2015    Cisco  and/or  its  affiliates.  All  rights  reserved.      Cisco  Confiden;al   •  Mine (fetch) •  Analyze •  ReportUsage Data ! Generate an Actionable Event that is sent to the Policy System, Management System, etc. to allow immediate control Next Generation Analytics Applies predicates, aggregations, and joins with metadata tables and contextual data to identify and match trends. Querybase Waiting for Data Store raw data or filtered data for further mining. Database Waiting for Queries Store raw data for further mining. Traditional Analytics Model Store first, and query later. Usage Data •  Mine (fetch) •  Analyze •  Report Connected Streaming Analytics
  • 23. ©  2015    Cisco  and/or  its  affiliates.  All  rights  reserved.      Cisco  Confiden;al   Make your data work for you Make it actionable in real time Make it scale without sacrificing latency Integrate advanced predictive analytics and machine learning Transparently combine both live and historic data Value of Real-Time Connected Streaming Analytics
  • 24. ©  2015    Cisco  and/or  its  affiliates.  All  rights  reserved.      Cisco  Confiden;al                     Cloud Data SourcesBig Data / IOE SourcesTraditional Data Sources AnalyticsBusiness Intelligence Cisco Data Virtualization Abstrac;on   Caching   Directory  Federa;on   Security   Governance  Transforma;on   Cisco Data Virtualization
  • 25. ©  2015    Cisco  and/or  its  affiliates.  All  rights  reserved.      Cisco  Confiden;al   On-demand Access Easier and Faster Up to 75% Cost Savings Cisco Data Virtualization More AgileHigher Impact Less Expensive Cisco Data Virtualization Better Business Outcomes, Faster, for Less
  • 26. ©  2015    Cisco  and/or  its  affiliates.  All  rights  reserved.      Cisco  Confiden;al   Analytics 3.0 Seconds/Milliseconds Traditional Data Warehouse Big Data Store DV Real-time/ Streaming −  Cloud −  Data Center −  Fog and Edge Connecting Distributed Data from the Data Center to the Edge Analytics 3.0 Requires a New Approach
  • 27.
  • 28. Twitter Tag: #briefr The Briefing Room Perceptions & Questions Analyst: Robin Bloor
  • 29. Then the Data Lake evaporated into the Cloud Moving Stuff Robin Bloor, PhD
  • 30. The Architecture of Motion Move the DATA to the processing OR Move the PROCESSING to the data OR Move the processing AND the data OR Shard and move
  • 31. The Global Picture u  IoT (embedded) u  IoT depots u  Wearables u  Mobile devices u  Web sites (depots) u  Desktops u  Data centers u  Cloud (depots) u  The network(s) All can be data creators, data stores and processing points. All should be state machines.
  • 32. The Target(s) These generalized targets are probably universal u  The necessary or best response time u  Appropriate availability up to full fault tolerance u  Portability - distribution u  Affordable cost of operation (for the benefit delivered)
  • 33. Distributed Processing u  The mechanisms for this are caching and virtualization u  Sharding involves the caching or virtualization of specific fragments (imagine virtualizing all or part of Hadoop) u  The management of this requires the software to be infrastructure-aware u  Service levels need to be specifically defined u  It is made even more complex by the reality that all these resources are shared
  • 34. Network-Aware Applications Ultimately we will have INFRASTRUCTURE-AWARE software that distributes data and applications
  • 35. u  I agree with the Cisco vision. But where has Cisco applied this thus far? What use cases can you tell us about? u  Traditionally Cisco is hardware and networking infrastructure. Is the company going soft? If so, is this just for the Big Data business? u  What are the security components that Cisco brings to the game? u  Global directory?
  • 36. u  Which vendors are you actively partnering with to deliver this vision? u  How easy is this? Can you discuss the nature of a real-world deployment of these capabilities? u  Is the IoT reference model a blueprint for all distributed infrastructure and supporting software?
  • 37. Twitter Tag: #briefr The Briefing Room
  • 38. Twitter Tag: #briefr The Briefing Room Upcoming Topics www.insideanalysis.com March: BI/ANALYTICS April: BIG DATA May: CLOUD
  • 39. Twitter Tag: #briefr The Briefing Room THANK YOU for your ATTENTION! Some images provided courtesy of Wikimedia Commons and Wikipedia, including: "Castello Fénis" by Rollopack - Own work. Licensed under CC BY-SA 3.0 via Wikimedia Commons - http:// commons.wikimedia.org/wiki/File:Castello_F%C3%A9nis.jpg#mediaviewer/File:Castello_F%C3%A9nis.jpg