Best practice 2.3 – Test and validate analytics jobs and application deployments - Data Analytics Lens
Suggestion 2.3.1 – Establish separate staging environments to test changes before going liveSuggestion 2.3.2 – Automate the deployment and testing when infrastructure and applications changes are introduced

Best practice 2.3 – Test and validate analytics jobs and application deployments

Before making changes in production environments, use standard and repeatable automated tests to validate performance and accuracy of results.

Suggestion 2.3.1 – Establish separate staging environments to test changes before going live

Use separate environments, such as development, test, and production, to allow feature development to be introduced without disrupting production systems. Test changes for accuracy and performance before changes are deployed into the production environment.

Suggestion 2.3.2 – Automate the deployment and testing when infrastructure and applications changes are introduced

The deployment of data pipelines and data infrastructure changes should be an automated process. When code is checked into version control, a CI/CD process should run tests and apply the changes to the staging environment, and once tested and confirmed correct, it should be deployed to the production environment.

You can use the AWS CodePipeline service to define a CI/CD process.

For more details refer to the following information: