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Accelerating DevSecOps on AWS

You're reading from   Accelerating DevSecOps on AWS Create secure CI/CD pipelines using Chaos and AIOps

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Product type Paperback
Published in Apr 2022
Publisher Packt
ISBN-13 9781803248608
Length 520 pages
Edition 1st Edition
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Author (1):
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Nikit Swaraj Nikit Swaraj
Author Profile Icon Nikit Swaraj
Nikit Swaraj
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Table of Contents (15) Chapters Close

Preface 1. Section 1:Basic CI/CD and Policy as Code
2. Chapter 1: CI/CD Using AWS CodeStar FREE CHAPTER 3. Chapter 2: Enforcing Policy as Code on CloudFormation and Terraform 4. Chapter 3: CI/CD Using AWS Proton and an Introduction to AWS CodeGuru 5. Section 2:Chaos Engineering and EKS Clusters
6. Chapter 4: Working with AWS EKS and App Mesh 7. Chapter 5: Securing Private EKS Cluster for Production 8. Chapter 6: Chaos Engineering with AWS Fault Injection Simulator 9. Section 3:DevSecOps and AIOps
10. Chapter 7: Infrastructure Security Automation Using Security Hub and Systems Manager 11. Chapter 8: DevSecOps Using AWS Native Services 12. Chapter 9: DevSecOps Pipeline with AWS Services and Tools Popular Industry-Wide 13. Chapter 10: AIOps with Amazon DevOps Guru and Systems Manager OpsCenter 14. Other Books You May Enjoy

Introduction to Amazon CodeGuru

In the software development life cycle, a code review process takes place when all the developers write their code and raise a pull request to merge to an upstream branch. The code review is generally done by the team leader of the project, but it could be a slow process to eyeball the entire code. The code review process is important, but it shouldn't increase the workload for reviewers and become a bottleneck in development. By using code review tools, we can automate the process of reviewing the code. Some famous tools in the market do this magic for us, such as SonarQube. Recently, Amazon launched a new service called Amazon CodeGuru, which can perform code reviews as well as provide application performance. This not only helps in improving the reliability of the software but also lets us dig deep and cut down on the time spent finding difficult issues, such as sensitive data, race conditions, undefined functions, and slow resource leaks.

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