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Strategizing Continuous Delivery in the Cloud

You're reading from   Strategizing Continuous Delivery in the Cloud Implement continuous delivery using modern cloud-native technology

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Product type Paperback
Published in Aug 2023
Publisher Packt
ISBN-13 9781837637539
Length 208 pages
Edition 1st Edition
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Authors (2):
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Garima Bajpai Garima Bajpai
Author Profile Icon Garima Bajpai
Garima Bajpai
Thomas Schuetz Thomas Schuetz
Author Profile Icon Thomas Schuetz
Thomas Schuetz
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Table of Contents (18) Chapters Close

Preface 1. Part 1: Foundation and Preparation for Continuous Delivery in the Cloud
2. Chapter 1: Planning for Continuous Delivery in the Cloud FREE CHAPTER 3. Chapter 2: Understanding Cloud Delivery Models 4. Chapter 3: Creating a Successful Strategy and Preparing for Continuous Delivery 5. Chapter 4: Setting Up and Scaling Continuous Delivery in the Cloud 6. Part 2: Implementing Continuous Delivery
7. Chapter 5: Finding Your Technical Strategy Toward Continuous Delivery in the Cloud 8. Chapter 6: Achieving Successful Implementation with Supporting Technology 9. Chapter 7: Aiming for Velocity and Reducing Delivery Risks 10. Chapter 8: Security in Continuous Delivery and Testing Your Deployment 11. Part 3: Best Practices and the Way Ahead
12. Chapter 9: Best Practices and References 13. Chapter 10: Future Trends of Continuous Delivery 14. Chapter 11: Contributing to the Open Source Ecosystem 15. Chapter 12: Practical Assignments 16. Index 17. Other Books You May Enjoy

The role of velocity in CD and associated risks

Deployments are events on a system that can have a huge impact on it. In traditional infrastructures, this thesis led to the assumption that it’s safer to deploy in larger batches and less often. Such deployments were called Big Bang deployments and were often done in the middle of the night. This was done to reduce the risk of failure and to have a higher chance of success. Let’s inspect this a bit:

  • Larger batches: If the changeset of software gets larger, the risk of failure and misbehavior increases, and our options when a change has a negative impact on customers get fewer. When a problem in a larger batch occurs, we might have a long changelog to investigate, and it might get very hard to find the root cause of the problem. Furthermore, it might be hard for the developers to recapitulate how they implemented a feature, as they may have implemented it a long time ago. Last, but not least, it might become difficult...
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