a connected data landscape: virtualization and the internet of things
TRANSCRIPT
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
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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
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Topics
March: BI/ANALYTICS
April: BIG DATA
May: CLOUD
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An Inflection Point for Data
RETHINK your architecture
RECAST your opportunities
REDEFINE your business
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Analyst: Robin Bloor
Robin Bloor is Chief Analyst at The Bloor Group
[email protected] @robinbloor
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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
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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
Bus
ines
s O
utco
mes
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?
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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/Seconds Days/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
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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 Consistency Data Mobility Optimized Form Factors
UCS Mini
UCS Mini
UCS for Enterprise
UCS for Hadoop
Nexus Family
ISR
APIC EM
AP MS
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 • Report Usage 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 scale without sacrificing latency
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 Sources Big Data / IOE Sources Traditional Data Sources
Analytics Business Intelligence
Cisco Data Virtualization Abstrac;on Caching Directory Federa;on Security Governance Transforma;on
Cisco Data Virtualization
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On-demand Access
Easier and Faster
Up to 75% Cost Savings
Cisco Data Virtualization
More Agile Higher 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
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Perceptions & Questions
Analyst: Robin Bloor
Then the DataLake evaporatedinto 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?
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Upcoming Topics
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March: BI/ANALYTICS
April: BIG DATA
May: CLOUD
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