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Automating DevOps with GitLab CI/CD Pipelines

You're reading from   Automating DevOps with GitLab CI/CD Pipelines Build efficient CI/CD pipelines to verify, secure, and deploy your code using real-life examples

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Product type Paperback
Published in Feb 2023
Publisher Packt
ISBN-13 9781803233000
Length 348 pages
Edition 1st Edition
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Authors (3):
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Chris Timberlake Chris Timberlake
Author Profile Icon Chris Timberlake
Chris Timberlake
Christopher Cowell Christopher Cowell
Author Profile Icon Christopher Cowell
Christopher Cowell
Nicholas Lotz Nicholas Lotz
Author Profile Icon Nicholas Lotz
Nicholas Lotz
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Table of Contents (18) Chapters Close

Preface 1. Part 1 Getting Started with DevOps, Git, and GitLab
2. Chapter 1: Understanding Life Before DevOps FREE CHAPTER 3. Chapter 2: Practicing Basic Git Commands 4. Chapter 3: Understanding GitLab Components 5. Chapter 4: Understanding GitLab’s CI/CD Pipeline Structure 6. Part 2 Automating DevOps Stages with GitLab CI/CD Pipelines
7. Chapter 5: Installing and Configuring GitLab Runners 8. Chapter 6: Verifying Your Code 9. Chapter 7: Securing Your Code 10. Chapter 8: Packaging and Deploying Code 11. Part 3 Next Steps for Improving Your Applications with GitLab
12. Chapter 9: Enhancing the Speed and Maintainability of CI/CD Pipelines 13. Chapter 10: Extending the Reach of CI/CD Pipelines 14. Chapter 11: End-to-End Example 15. Chapter 12: Troubleshooting and the Road Ahead with GitLab 16. Index 17. Other Books You May Enjoy

Running GitLab CI/CD pipelines

Whenever a project’s pipeline runs, it’s running on some version of that project’s files. This means that in the CI portion of the pipeline, it runs automated tests and scans on just one version of your files. Then, in the CD portion, it deploys that same version of the files to the appropriate environment. You will also see this described as a pipeline running “against” a version of your project’s files.

The point of pipelines is to check the status of your code – and then deploy that code – every time you make changes to it. So, running a project’s pipeline on yesterday’s version of your code may produce one set of results, while running the pipeline against today’s version of the code may generate very different results, even though the pipeline consists of the same stages, jobs, and commands. Between yesterday and today, you may have added new automated tests, introduced...

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