What is the MOST efficient way to meet these requirements?
Create an AWS CodeCommit repository for each project, use the main branch for production code, and create a testing branch for code deployed to testing. Use feature branches to develop new features and pull requests to merge code to testing and main branches.
Create another S3 bucket for each project for testing code, and use an AWS Lambda function to promote code changes between testing and production buckets. Enable versioning on all buckets to prevent code conflicts.
Create an AWS CodeCommit repository for each project, and use the main branch for production and test code with different deployment pipelines for each environment. Use feature branches to develop new features.
Enable versioning and branching on each S3 bucket, use the main branch for production code, and create a testing branch for code deployed to testing. Have developers use each branch for developing in each environment.
Explanations:
AWS CodeCommit is a fully managed version control service that supports branching, pull requests, and collaboration, making it ideal for managing code changes. This option ensures clear separation between environments (production and testing) and allows for efficient collaboration using feature branches.
Storing code in S3 buckets is not an efficient way to manage source code for version control, as S3 is not designed for collaborative development. Additionally, Lambda for promoting changes adds unnecessary complexity compared to a version-controlled system like CodeCommit.
While CodeCommit is appropriate, having separate pipelines for testing and production is useful. However, mixing production and test code in the same branch can create unnecessary risk. It’s better to have distinct branches for production and testing.
S3 is not an ideal tool for version control and branching. Versioning and branching in S3 would not provide the full functionality and collaboration features that CodeCommit offers. Additionally, managing code directly in S3 can lead to conflicts and inefficiencies.
I opine that the answer is:
Create an AWS CodeCommit repository for each project, use the main branch for production code, and create a testing branch for code deployed to testing. Use feature branches to develop new features and pull requests to merge code to testing and main branches.