A Connected Data Landscape: Virtualization and the Internet of Things

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banter before

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The Briefing Room

A Connected Data Landscape: Virtualization and the Internet of Things

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Welcome

Host: Eric Kavanagh

eric.kavanagh@bloorgroup.com @eric_kavanagh

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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

robin.bloor@bloorgroup.com @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?

©  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/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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March: BI/ANALYTICS

April: BIG DATA

May: CLOUD

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