Darren Pulsipher, Federal Chief Solutions Architect, Intel 2020 Data Cloud Summit

1. History of Data-Centric
Transformation
DARREN W PULSIPHER
CHIEF SOLUTION ARCHITECT

2. “Edge” Data Creating Wave of End-to-End Implications
SECURITY, COMPLIANCE, REGULATIONS
Things EdgeCompute Network
Infrastructure
DataCenter/
Cloud

3. Mission Integration
Enterprise Data Center
LogisticsAnalytics
Flow
Security Data
Store
Public Clouds
Visualization
Analysts
Ground Stations
TOC
Aggregated Edge
Regional Node
Smart Installation

4. Deploying Applications
Enterprise Data Center
LogisticsAnalytics
Flow
Security
Data Store
Public Clouds
Visualization
Analysts
Ground Stations
TOC
Aggregated Edge
Regional Node
Smart Installation
App1
App1
App1
App1
App1
App1

5. Deploying Applications
Enterprise Data Center
LogisticsAnalytics
Flow
Security
Data Store
Public Clouds
Visualization
Analysts
Ground Stations
TOC
Aggregated Edge
Regional Node
Smart Installation
App1
App1
App1
App1
App1
App1
App2
App2
App2
App3
App3
App3
App3
App4App4
App4

6. Problem with Mission deployments
• No common Physical Platform
• Every mission requires purpose build hardware or even COTs rework.
• Hardware is not being reused or used effectively
• Too long to deploy new applications for Missions
• Integration of applications is difficult if not impossible.
• Large attack surface increases security concerns.

7. Elements of Change
• People (Organizational)
• Process (Operational)
• Architecture (Technological)
Operational
TechnologicalOrganizational

8. Purpose Built Software and Hardware
Stack
Purpose Built Software and Hardware
Stack
Building Applications for Missions
Application 1 Data
Compute Storage Network
Application 2 Data
Compute Storage
• Long Development Times
• No re-use of technology
• Technology moves too fast
• Hard to integrate as Technology Drifts
• Cost increases due to lack of reuse

9. Hardware Virtualization
Application 1 Data
Compute Storage Network
Application 2 Data
• Common Hardware re-use
• Decrease Cost
• Security concerns increase
• Noisy neighbors
Virtual
Machine
Virtual
MachineVSAN VSANNVF NVF

10. Private Clouds
Cloud Technology (Operations Abstraction)
Application 1 Data
Compute Storage Network
Application 2 Data
• Common Hardware re-use
• Decrease Cost OpEx and CapEx
• Bursting ability
• Security concerns increase
• Noisy neighbors
• Integration costs
VM VMVSAN VSANNVF NVF
Application n Data
Public Clouds

11. Private CloudsPrivate Clouds
Service and Container Technology
Application 1 Data
Compute Storage Network
Application 2 Data
• Automatic deployment across Clouds
• Optimized OpEx and CapEx
• Fault tolerance
• Easier Integration
• Security concerns increase
• Increased complexity
• Where’s my data
VM VMVSAN VSANNVF NVF
Application n Data
Public Clouds
Public Clouds
Service Management Layer
Containers
Overlay
NetworkVolume Containers Volume
Overlay
Network
Containers VolumeContainers Containers Containers

12. Private CloudsPrivate Clouds
Internet of Things
Application 1 Data
Compute Storage Network
Application 2 Data
• Increase the amount of Data
• Visibility increases
• Security concerns increase
• Increased complexity
• Where’s my data
VM VMVSAN VSANNVF NVF
Application n Data
Public Clouds
Public Clouds
Service Management Layer
Containers
Overlay
NetworkVolume Containers Volume
Overlay
Network
Containers VolumeContainers Containers Containers
IoT

13. Organizations are Changing
Data ScientistData Steward
IT Ops Data Engineer
CDO App Dev
Manage
Infrastructure
Manage
Data / Policies
Set Data Policy
& Strategy
Develop
Apps
Analyze Data
Derive Information
Manage
Sources, Blue Prints,
& Procedures
Distributed Information
Management Layer

14. Distributed Information Management Layer
Private CloudsPrivate Clouds
Data and Information Management
Application 1 Data
Compute Storage Network
Application 2 Data
• Automatic data management
• Data Locality
• Data Governance
• Security concerns increase
• Classification Profiles Not supported
VM VMVSAN VSANNVF NVF
Application n Data
Public Clouds
Public Clouds
Service Management Layer
Net
V
C C CV VNet
IoT
Meta-Data
Data
Orchestration
Data Mgmt

15. Security and Identity
• Common Identity (People, Devices, Software)
• Access, Authorization, Authentication
• Detect attacks throughout the ecosystem
• Establish root of trust from through the stack
• Quarantine with reliability
Distributed Information Management Layer
Private Clouds
Private Clouds
Application 1 Data
Compute Storage Network
Application 2 Data
VM VMVSAN VSANNVF NVF
Application n Data
Public Clouds
Public Clouds
Service Management Layer
Net
V
C C CV VNet
IoT
Meta-Data
Data
Orchestrator
Identity
Aspect
Security
Aspect
Access
Authorize
Authenticate
Key
Management
Root of
Trust
Detect
Remediate
Encrypt
Data Mgmt

16. Edgemere Architectural View
Common Physical Layer
Application Layer
Distributed Information
Management
Layer
Service
Management Layer
Software Defined Infrastructure
SecurityAspect
IdentityAspect

17. Common Physical Platform
Accelerates Data-Centric Transformation
SILICON PHOTONICS
OMNI-PATH FABRIC
ETHERNET
DC SERIES SSDQ L C 3 D N A N D D R I V E
INTEL ATOM® C PROCESSORS
INTEL® NERVANA™ NNP
INTEL® MOVIDIUS™
INTEL® XEON® D PROCESSORS
INTEL® XEON® SCALABLE
INTEL® FPGAS

18. Call to action
• Develop your Data Strategy
• People (Organizational Change)
• Process (Data Processes and Governance)
• Technology (Data Architecture)
• Contact
darren.w.pulsipher@intel.com
@darrenpulsipher

19. Data-Centric Transformation