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End-to-End Automation with Kubernetes and Crossplane

You're reading from   End-to-End Automation with Kubernetes and Crossplane Develop a control plane-based platform for unified infrastructure, services, and application automation

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Product type Paperback
Published in Aug 2022
Publisher Packt
ISBN-13 9781801811545
Length 250 pages
Edition 1st Edition
Concepts
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Author (1):
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Arun Ramakani Arun Ramakani
Author Profile Icon Arun Ramakani
Arun Ramakani
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Toc

Table of Contents (16) Chapters Close

Preface 1. Part 1: The Kubernetes Disruption
2. Chapter 1: Introducing the New Operating Model FREE CHAPTER 3. Chapter 2: Examining the State of Infrastructure Automation 4. Part 2: Building a Modern Infrastructure Platform
5. Chapter 3: Automating Infrastructure with Crossplane 6. Chapter 4: Composing Infrastructure with Crossplane 7. Chapter 5: Exploring Infrastructure Platform Patterns 8. Chapter 6: More Crossplane Patterns 9. Chapter 7: Extending and Scaling Crossplane 10. Part 3:Configuration Management Tools and Recipes
11. Chapter 8: Knowing the Trade-offs 12. Chapter 9: Using Helm, Kustomize, and KubeVela 13. Chapter 10: Onboarding Applications with Crossplane 14. Chapter 11: Driving the Platform Adoption 15. Other Books You May Enjoy

Other similar projects

A few other Kubernetes-based infrastructure automation projects share common interests and support similar use cases such as Crossplane. These projects extend Kubernetes with APIs and custom controllers identical to Crossplane architecture. This section will look at those tools to have a comprehensive comparison with Crossplane. The following is a list of a few such projects:

  • Kubernetes’ Service Catalog by the open service broker enables life cycle management of cloud resources from Kubernetes. Like Crossplane, it works as a Kubernetes controller extension. But it does not have a solid framework to compose infrastructure recipes with policy guardrails. Also, we can’t model the API for different team boundaries. The open service broker Kubernetes Service Catalog is not designed for platform teams to build reusable recipes with encoded policies. Typically, this means that we have to struggle with policy enforcement and a high cognitive load...
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