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Dev348 ReInvent Corteva Agriscience
- 2. © 2018, Amazon Web Services, Inc. or its affiliates. All rights reserved. RemoveUndifferentiated Heavy Lifting fromCI/CDToolsets Randy Black Lead Cloud Architect Corteva Agriscience™ D E V 3 4 8 Duke Takle Principal Architect CMR Solutions Nathan Taber Sr. Product Marketing Manager AWS
- 3. © 2018, Amazon Web Services, Inc. or its affiliates. All rights reserved. Our leadershipprinciples shapeour approach Customer obsession Start with the customer and work backwards Earn and keep customer trust Invent and simplify Always find ways to simplify Look for new ideas everywhere Bias for action Speed matters in business We value calculated risk taking
- 4. © 2018, Amazon Web Services, Inc. or its affiliates. All rights reserved. “ ” — © 2018, Amazon Web Services, Inc. or its affiliates. All rights reserved.
- 5. © 2018, Amazon Web Services, Inc. or its affiliates. All rights reserved. Howwefocus on thecustomer ExperimentIterate Listen Innovation flywheel
- 6. © 2018, Amazon Web Services, Inc. or its affiliates. All rights reserved. Amazon builds for rapid experimentation Try a lot of experiments Eliminate the collateral damage from failed experiments
- 7. © 2018, Amazon Web Services, Inc. or its affiliates. All rights reserved. We build for rapid experimentation by… • Simplifying infrastructure management • Componentizing applications • Standardizing and automating operations • Improving application performance • Creating a culture of innovation • Updating applications and infrastructure quickly • Ensuring trust This is modern application development
- 8. © 2018, Amazon Web Services, Inc. or its affiliates. All rights reserved. HowAmazon builds and runs modern applications Microservices Componentization via services Organized around business capabilities Products not projects Infrastructure automation Serverless No infrastructure to provision or manage Auto scale by unit of consumption Pay for value billing model Built-in availability and fault tolerance DevOps Cultural philosophies Cross-disciplinary teams CI/CD Automation tools
- 9. © 2018, Amazon Web Services, Inc. or its affiliates. All rights reserved. Companiesineverysegmentarebuildingmodernapplications EnterprisesStartups B2B B2C Verticals
- 10. © 2018, Amazon Web Services, Inc. or its affiliates. All rights reserved. Companiesineverysegmentarebuildingmodernapplications Agriculture
- 11. © 2018, Amazon Web Services, Inc. or its affiliates. All rights reserved.
- 12. © 2018, Amazon Web Services, Inc. or its affiliates. All rights reserved.
- 13. © 2018, Amazon Web Services, Inc. or its affiliates. All rights reserved. Undifferentiated HeavyLifting “Stop spending money on undifferentiated heavy lifting.” “All this heavy lifting is taken out of your hands” “We thrive on feedback how to build better services.” Dr. Werner Vogels, CTO of Amazon https://www.cio.co.nz/article/466635/amazon_cto_stop_spending_money_undifferentiated_heavy_lifting_/
- 14. © 2018, Amazon Web Services, Inc. or its affiliates. All rights reserved. ManagedServices What makes them worth while • relatively easy to implement • little to zero fte involvement to maintain • pay as you use
- 15. © 2018, Amazon Web Services, Inc. or its affiliates. All rights reserved. ManagedServices How to choose • does the service exist • what is your expectation of the service • what percentage of needs are met • we us e a goal of 80%
- 16. © 2018, Amazon Web Services, Inc. or its affiliates. All rights reserved. Whatdrivers effectour decisions
- 17. © 2018, Amazon Web Services, Inc. or its affiliates. All rights reserved. Whatdrivers effectour decisions • large and more varied data types
- 18. © 2018, Amazon Web Services, Inc. or its affiliates. All rights reserved. Whatdrivers effectour decisions • large and more varied data types • scalable and flexible
- 19. © 2018, Amazon Web Services, Inc. or its affiliates. All rights reserved. Whatdrivers effectour decisions • large and more varied data types • scalable and flexible • operational excellence
- 20. © 2018, Amazon Web Services, Inc. or its affiliates. All rights reserved. Whatdrivers effectour decisions • large and more varied data types • scalable and flexible • operational excellence • easily adoptable
- 21. © 2018, Amazon Web Services, Inc. or its affiliates. All rights reserved. • low cost • zero maintenance • little to zero downtime provider SLA • faster realization - months to days • trivial implementation • rapid change and deploy/redeploy • no context shift – focus for developers Observed Benefits
- 22. © 2018, Amazon Web Services, Inc. or its affiliates. All rights reserved. • slow adoption • not built here • inexperience • myopic • in-house options expensive • cloud is hard Lessons Learned
- 23. © 2018, Amazon Web Services, Inc. or its affiliates. All rights reserved. Continuing our process
- 24. © 2018, Amazon Web Services, Inc. or its affiliates. All rights reserved. Continuing our process • more active guidance and evangelism
- 25. © 2018, Amazon Web Services, Inc. or its affiliates. All rights reserved. Continuing our process • more active guidance and evangelism • continued feature requests; make them known to AWS
- 26. © 2018, Amazon Web Services, Inc. or its affiliates. All rights reserved. Continuing our process • more active guidance and evangelism • continued feature requests; make them known to AWS • results are in - other teams/projects help drive adoption
- 27. © 2018, Amazon Web Services, Inc. or its affiliates. All rights reserved. Duke Takle Principal Architect CMR Solutions Slides Available at https://www.slideshare.net/RandyBlack1 Code Available at https://github.com/cmrsol/example
- 28. © 2018, Amazon Web Services, Inc. or its affiliates. All rights reserved. Problem Implement a highly available genetic analysis API
- 29. © 2018, Amazon Web Services, Inc. or its affiliates. All rights reserved. Implement ahighlyavailablegenetic analysisAPI • proof-of-concept had been done with a Docker container • must avoid Undifferentiated Heavy Lifting • solution must be totally defined in code • solution must be containerized • solution must be automatically built and deployed • developers may have control of ci/cd and visibility into orchestrated process
- 30. © 2018, Amazon Web Services, Inc. or its affiliates. All rights reserved. Solution -GeneticAnalysisAPI This is the “secret sauce” part of the problem
- 31. © 2018, Amazon Web Services, Inc. or its affiliates. All rights reserved. AWSAccountStructure
- 32. © 2018, Amazon Web Services, Inc. or its affiliates. All rights reserved. AWSAccountStructure ● utility account for management ● deployed from utility account ● hub and spoke design
- 33. © 2018, Amazon Web Services, Inc. or its affiliates. All rights reserved. AWSAccountStructure ● utility account for management ● deployed from utility account ● hub and spoke design
- 34. © 2018, Amazon Web Services, Inc. or its affiliates. All rights reserved. AWSAccountStructure ● utility account for management ● deployed from utility account ● hub and spoke design
- 35. © 2018, Amazon Web Services, Inc. or its affiliates. All rights reserved. AWSAccountStructure ● exports live in all accounts
- 36. © 2018, Amazon Web Services, Inc. or its affiliates. All rights reserved. AWSAccountStructure ● parametrized CloudFormation templates
- 37. © 2018, Amazon Web Services, Inc. or its affiliates. All rights reserved. CompletelyDefined inCode ● Strong bias toward CloudFormation ○ Layered and connected with imports/exports ○ One static template for all environments ● When CloudFormation falls short, boto3 ○ Number of libraries and utilities ○ Key is extracting commonality to utilities ● Use of console outside “sandbox” is discouraged
- 38. © 2018, Amazon Web Services, Inc. or its affiliates. All rights reserved. Containerized ● Containers are cliché for a reason ● Our images built/stored in the central account’s ECR ● Images promoted not rebuilt in our SDLC ● This solution used an ECS Fargate Cluster
- 39. © 2018, Amazon Web Services, Inc. or its affiliates. All rights reserved. Automated Build & Deploy
- 40. © 2018, Amazon Web Services, Inc. or its affiliates. All rights reserved. Automated Build & Deploy
- 41. © 2018, Amazon Web Services, Inc. or its affiliates. All rights reserved. Automated Build & Deploy
- 42. © 2018, Amazon Web Services, Inc. or its affiliates. All rights reserved. Automated Build & Deploy
- 43. © 2018, Amazon Web Services, Inc. or its affiliates. All rights reserved. Automated Build & Deploy
- 44. © 2018, Amazon Web Services, Inc. or its affiliates. All rights reserved. Automated Build & Deploy
- 45. © 2018, Amazon Web Services, Inc. or its affiliates. All rights reserved. Automated Build & Deploy
- 46. © 2018, Amazon Web Services, Inc. or its affiliates. All rights reserved. DetailedExample ● The rest of the time...
- 47. © 2018, Amazon Web Services, Inc. or its affiliates. All rights reserved. AWSConsole Detail
- 48. © 2018, Amazon Web Services, Inc. or its affiliates. All rights reserved. Images at: http://static.mknote.us/re/invent2018
- 49. © 2018, Amazon Web Services, Inc. or its affiliates. All rights reserved. Images at: http://static.mknote.us/re/invent2018
- 50. © 2018, Amazon Web Services, Inc. or its affiliates. All rights reserved. Images at: http://static.mknote.us/re/invent2018
- 51. © 2018, Amazon Web Services, Inc. or its affiliates. All rights reserved. Images at: http://static.mknote.us/re/invent2018
- 52. © 2018, Amazon Web Services, Inc. or its affiliates. All rights reserved. the anatomy of most of my pipelines: • 2 … n stages • each stage has 1 … n actions that do the real work • actions are where the CodeBuild things are put into play Create the CodePipeline
- 53. © 2018, Amazon Web Services, Inc. or its affiliates. All rights reserved.
- 54. © 2018, Amazon Web Services, Inc. or its affiliates. All rights reserved.
- 55. © 2018, Amazon Web Services, Inc. or its affiliates. All rights reserved.
- 56. © 2018, Amazon Web Services, Inc. or its affiliates. All rights reserved.
- 57. © 2018, Amazon Web Services, Inc. or its affiliates. All rights reserved.
- 58. © 2018, Amazon Web Services, Inc. or its affiliates. All rights reserved.
- 59. © 2018, Amazon Web Services, Inc. or its affiliates. All rights reserved. What happens in all the CodeBuild projects
- 60. © 2018, Amazon Web Services, Inc. or its affiliates. All rights reserved.
- 61. © 2018, Amazon Web Services, Inc. or its affiliates. All rights reserved. Describing what happens in all the CodeBuild projects
- 62. © 2018, Amazon Web Services, Inc. or its affiliates. All rights reserved.
- 63. © 2018, Amazon Web Services, Inc. or its affiliates. All rights reserved. Slides Available at https://www.slideshare.net/RandyBlack1 Code Available at https://github.com/cmrsol/example
- 64. Thank you! © 2018, Amazon Web Services, Inc. or its affiliates. All rights reserved. Randy Black randy.black@pioneer.com Duke Takle duke@cmrsol.com
Editor's Notes
- So what are the characteristics of a digital innovator? Well, first - they work backwards from a customer’s point of view. They are continuously evolving products and services to better deliver the outcomes that their customers need. They listen to what customers care about, and invent and iterating on their behalf And they keep inventing and releasing more services that solve customers’ problems faster than the competition As our CTO Werner Vogels puts it, ““The driver for which services to build and which features to add is done by customers,” he says. “We thrive on feedback how to build better services.” Source Amazon CTO: Stop spending money on 'undifferentiated heavy lifting‘, CIO Magazine, June 2013 https://www.cio.co.nz/article/466635/amazon_cto_stop_spending_money_undifferentiated_heavy_lifting_/
- Innovation requires: 1/ the ability to try a lot of experiments 2/ not having to live with the collateral damage of failed experiments. This is all enticing and where a lot of us want to be. But it turns out that it’s also really hard. When we look at all of our customers, and at our own experience, we see the same two challenges come up over and over again Too much time is spent on things that don’t deliver business value You have to integrate with your existing repertoire of applications
- Every company, regardless of size, transaction model, or industry, faces the opportunity for digital transformation. This includes small startups as well as large, established organizations in virtually every industry Thousands of digital startups are already doing this, and so are innovative B2C companies such as Hasbro, and The Washington Post and B2B giants like John Deere, SAP and NEC. Digital customer engagement is common across all industries. Manufacturers are digitizing their products, manufacturing, and supply chains, Retailers are transforming the role of stores in customer loyalty and engagement, Banks are building digital platforms to embed their services into partners’ services, and Media companies are ramping up to create on-demand entertainment platforms. All of us are looking to provide new and better customer experiences.
- Even, Agriculture
- As a consumer we use AWS and cloud resources to take advantage of technology cloud providers offer and continue to build, promote and mature Building in-house is more time consuming, more expensive and comes with the associated risk, your talent may leave with the only known knowledge Managed services allows us to use technology to build faster and better than we can do in-house, reducing complexity and development and engineering time as well as hire talent with the knowledge or learn the standard knowledge to support the cloud feature
- easy to implement little to zero fte involvement pay as you use
- - Does the service exist - what is your expectation of the service - does it meet all you needs or a percentage of 80/90/100 - 80 % isa good goal for us - is it actively under development, how many contributors, does it have ”long life”
- - is it actively under development, how many contributors, does it have ”long life” Easily implemented Quickly adopted with little training (matches the current ecosystem, practices and principles) Cost Effective – You no longer have a requirement to in-house the process/technology Each organization has it’s own guiding drivers to make decisions Image - http://circos.ca/intro/genomic_data/img/circos-conde-nast-large.png http://circos.ca/intro/genomic_data/
- - is it actively under development, how many contributors, does it have ”long life” Easily implemented Quickly adopted with little training (matches the current ecosystem, practices and principles) Cost Effective – You no longer have a requirement to in-house the process/technology Each organization has it’s own guiding drivers to make decisions Image - http://circos.ca/intro/genomic_data/img/circos-conde-nast-large.png http://circos.ca/intro/genomic_data/ Genetics – larga and small, UAS images, Hyperspectral image, IoT inputs
- - is it actively under development, how many contributors, does it have ”long life” Easily implemented Quickly adopted with little training (matches the current ecosystem, practices and principles) Cost Effective – You no longer have a requirement to in-house the process/technology Each organization has it’s own guiding drivers to make decisions Image - http://circos.ca/intro/genomic_data/img/circos-conde-nast-large.png http://circos.ca/intro/genomic_data/
- - is it actively under development, how many contributors, does it have ”long life” Easily implemented Quickly adopted with little training (matches the current ecosystem, practices and principles) Cost Effective – You no longer have a requirement to in-house the process/technology Each organization has it’s own guiding drivers to make decisions Image - http://circos.ca/intro/genomic_data/img/circos-conde-nast-large.png http://circos.ca/intro/genomic_data/
- - is it actively under development, how many contributors, does it have ”long life” Easily implemented Quickly adopted with little training (matches the current ecosystem, practices and principles) Cost Effective – You no longer have a requirement to in-house the process/technology Each organization has it’s own guiding drivers to make decisions Image - http://circos.ca/intro/genomic_data/img/circos-conde-nast-large.png http://circos.ca/intro/genomic_data/
- How much time did we gain in the faster realization Rapid change and deploy / redeploy – this code also is reusable in many project decreasing time to realize
- Talk about cloud formation and hub and spoke design AWS Exports