How should a solutions architect improve the CI/CD pipeline to reduce the likelihood that changes in the templates will cause downtime?
Adapt the deployment scripts to detect and report CloudFormation error conditions when performing deployments. Write test plans for a testing team to run in a non-production environment before approving the change for production.
Implement automated testing using AWS CodeBuild in a test environment. Use CloudFormation change sets to evaluate changes before deployment. Use AWS CodeDeploy to leverage blue/green deployment patterns to allow evaluations and the ability to revert changes, if needed.
Use plugins for the integrated development environment (IDE) to check the templates for errors, and use the AWS CLI to validate that the templates are correct. Adapt the deployment code to check for error conditions and generate notifications on errors. Deploy to a test environment and run a manual test plan before approving the change for production.
Use AWS CodeDeploy and a blue/green deployment pattern with CloudFormation to replace the user data deployment scripts. Have the operators log in to running instances and go through a manual test plan to verify the application is running as expected.
Explanations:
While detecting and reporting CloudFormation errors and having a testing plan is valuable, it does not address automated deployment strategies. It relies heavily on manual processes which can still lead to downtime during changes.
This option provides a comprehensive solution by incorporating automated testing with AWS CodeBuild, using CloudFormation change sets to preview changes before deployment, and leveraging AWS CodeDeploy’s blue/green deployment to minimize downtime and allow for easy rollbacks.
Although using IDE plugins and AWS CLI validation is beneficial for error checking, it still relies on manual testing and does not automate the deployment process. This approach could lead to downtime if changes are not adequately tested in a production-like environment.
Utilizing AWS CodeDeploy and blue/green deployment patterns is a good approach; however, having operators manually verify the application on running instances introduces potential human error and increases the risk of downtime, making it less effective in automating the deployment process.