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Straightforward Capital and Advice for Technical Entrepreneurs
Enterprise Adoption of Cloud and DevOps
Divergent Ventures Managing Director Todd Warren
(www.divergentvc.com )twitter @toddwseattle
2
Agenda
• A short history of software development
• What technology and cultural trends are driving change
• Decisions and Puzzles in the current IT Landscape
• Scoring Cultural and Process Maturity Compared to Startups
3
Where I come from
Beginning
• Small Teams
• Simple Toolset
• Limited Dependencies
• Static Deliverables
• 12 to 18 mo cycles
Middle
• Massive Teams
• Networked Tools
• High Interdependency
• Semi-Dynamic Delivery
• 24month+ Cycles
Today
• 2 Pizza Teams
• Cloud
• Service Composition
• Continuous Delivery
• Weekly to Daily Cycles
4
Where We Came From
Source: US Military Computer Image Archive Courtesy of Michael John Muuss
5
60’s
• Compiler (Grace
Hopper)
• Large Scale Software
Systems
(Brooks/Humphreries)
70’s
• Modular, Portable
Systems (UNIX)
• Source Control
(SCCS)
• (Thompson,
Kernighan, Ritchie..)
80’s
• Waterfall
methodologies
• Modular and Object
oriented systems (Ada
etc.)
• Modeling Language
(e.g. UML)
90’s
• Open Source
Software
• Agile Methodologies
• Graphical
Development
Environments
• Patterns
2000’s
• ‘Data Center as the
Computer’
• Stateless Protocols
• Eventually
Consistent
A Brief History of Software Engineering
Today:
• ‘Dev Ops’
• Continuous Deployment
• Very Large Scale
Frameworks
• Cloud Services
• Distributed Source Code
Control
6
Tech Trends
Storage
Cheaper, Denser(<$.05/GB)
Faster (Flash Below $5/GB)
Data
Sensors
Machine Activity
Human Activity
Cloud
Elastic
Distributed
Need for Security
Utility Computing
Multi-Device
Mobile
Occasionally Connected
BYOD
7
Mobile
Networks
(10PB/Day)
Unmanned Aerial
Vehicles
(100PB/Day)
Yesterday’s Big Data
is today’s Small Data
8
Some of Divergent’s Bets
Value Trend
Hardware Independent
OpenStack Implementation
Private Cloud
Elastic Computing
Realtime Spatial Data Analysis
at Scale
Even Bigger Data
Sensors
GeoSpatial
Message Queueing
Asynchronous Task Management
Composable Web Services
Elastic Scale
On-Demand Computing
Distributed Security for
SQL and Hadoop
Secure Data Access
Hadoop
Hybrid Application Enablement
Social Proof at Scale
for Brands
Mining of Social Data
Composable Services
Next Generation eCommerce
9
Entrepreneurial API:
A cultural Revolution
+
+
Part 1
Part 2
Agile Engineering
Part 3
Build
MeasureLearn
• Focus on Value
• “Minimum Viable Product”
• Analytics
• A/B Testing
• Cohort Analysis
• Scale for Good Enough
• Refactor, Revise, Redeploy
• Swarm to solve problems
10
What does this mean for Enterprise?
• Business Value is and should drive Cloud implementation
• Better connection with customers
• Better Access to information for decisions
• Will Hybrid and Private Cloud Really Happen?
• Private Cloud Nearly Flat in 20141
• Yet companies still indicate they will implement
• Refactoring Applications for composability, hybrid enablement
Join the Divergent IT Council: http://www.divergentvc.com/itpro
1-Source:Rightscale 2015 state of the cloud
11
Is your development culture prepared for the
new world?
Using Git and Github for better Management of Projects
Continuous Integration and Continuous Deployment Mindset
Focus on Value Delivery: Build, Measure, Learn
Refactor to Enable
Compose vs. Build
12
Questions and Discussion
13
Contact Us:
http://www.divergentvc.com/itpro
Straightforward Capital and Advice for Technical Entrepreneurs
Office:
1652 20th Ave.
Seattle, WA 98122
Todd Warren (see http://toddwarreninc.com
Twitter: @toddwseattle
Contact Us: http://www.divergentvc.com/itpro

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A VC perspective on Devops and Enterprise Cloud Adoption

  • 1. Straightforward Capital and Advice for Technical Entrepreneurs Enterprise Adoption of Cloud and DevOps Divergent Ventures Managing Director Todd Warren (www.divergentvc.com )twitter @toddwseattle
  • 2. 2 Agenda • A short history of software development • What technology and cultural trends are driving change • Decisions and Puzzles in the current IT Landscape • Scoring Cultural and Process Maturity Compared to Startups
  • 3. 3 Where I come from Beginning • Small Teams • Simple Toolset • Limited Dependencies • Static Deliverables • 12 to 18 mo cycles Middle • Massive Teams • Networked Tools • High Interdependency • Semi-Dynamic Delivery • 24month+ Cycles Today • 2 Pizza Teams • Cloud • Service Composition • Continuous Delivery • Weekly to Daily Cycles
  • 4. 4 Where We Came From Source: US Military Computer Image Archive Courtesy of Michael John Muuss
  • 5. 5 60’s • Compiler (Grace Hopper) • Large Scale Software Systems (Brooks/Humphreries) 70’s • Modular, Portable Systems (UNIX) • Source Control (SCCS) • (Thompson, Kernighan, Ritchie..) 80’s • Waterfall methodologies • Modular and Object oriented systems (Ada etc.) • Modeling Language (e.g. UML) 90’s • Open Source Software • Agile Methodologies • Graphical Development Environments • Patterns 2000’s • ‘Data Center as the Computer’ • Stateless Protocols • Eventually Consistent A Brief History of Software Engineering Today: • ‘Dev Ops’ • Continuous Deployment • Very Large Scale Frameworks • Cloud Services • Distributed Source Code Control
  • 6. 6 Tech Trends Storage Cheaper, Denser(<$.05/GB) Faster (Flash Below $5/GB) Data Sensors Machine Activity Human Activity Cloud Elastic Distributed Need for Security Utility Computing Multi-Device Mobile Occasionally Connected BYOD
  • 8. 8 Some of Divergent’s Bets Value Trend Hardware Independent OpenStack Implementation Private Cloud Elastic Computing Realtime Spatial Data Analysis at Scale Even Bigger Data Sensors GeoSpatial Message Queueing Asynchronous Task Management Composable Web Services Elastic Scale On-Demand Computing Distributed Security for SQL and Hadoop Secure Data Access Hadoop Hybrid Application Enablement Social Proof at Scale for Brands Mining of Social Data Composable Services Next Generation eCommerce
  • 9. 9 Entrepreneurial API: A cultural Revolution + + Part 1 Part 2 Agile Engineering Part 3 Build MeasureLearn • Focus on Value • “Minimum Viable Product” • Analytics • A/B Testing • Cohort Analysis • Scale for Good Enough • Refactor, Revise, Redeploy • Swarm to solve problems
  • 10. 10 What does this mean for Enterprise? • Business Value is and should drive Cloud implementation • Better connection with customers • Better Access to information for decisions • Will Hybrid and Private Cloud Really Happen? • Private Cloud Nearly Flat in 20141 • Yet companies still indicate they will implement • Refactoring Applications for composability, hybrid enablement Join the Divergent IT Council: http://www.divergentvc.com/itpro 1-Source:Rightscale 2015 state of the cloud
  • 11. 11 Is your development culture prepared for the new world? Using Git and Github for better Management of Projects Continuous Integration and Continuous Deployment Mindset Focus on Value Delivery: Build, Measure, Learn Refactor to Enable Compose vs. Build
  • 14. Straightforward Capital and Advice for Technical Entrepreneurs Office: 1652 20th Ave. Seattle, WA 98122 Todd Warren (see http://toddwarreninc.com Twitter: @toddwseattle Contact Us: http://www.divergentvc.com/itpro

Editor's Notes

  1. A common reference point is the Twitter firehose. It has been a common benchmark for companies to come out and state that they can handle the live streaming of twitter data and analyze it. We solve problems on a higher order. Critical data is being produced by Mobile Networks and UAVs at a rate that far exceeds Twitter. Our customers require us to be able to ingest, store, and analyze this data in real time. All of this redefined Big Data has a geospatial and time element that needs to be woven together with other Big Data streams to make it meaningful. This is the problem we have solved. Uniquely.