aws-resource-validator-drs

Create a Python-based utility named 'DRSValidator' that leverages the 'aws-resource-validator-drs' package to validate AWS DRS (Disaster Recovery Service) resources. This tool will serve as a robust validation framework for ensuring that the configuration of your AWS DRS resources adheres to best practices and meets specific criteria defined by your organization.

### Features:
- **Resource Validation:** Implement a feature to validate individual AWS DRS resources against predefined Pydantic models provided by the 'aws-resource-validator-drs' package. Ensure that the validation process checks for compliance with AWS best practices.
- **Batch Validation:** Extend the functionality to support batch validation of multiple AWS DRS resources at once, providing a summary report of validation results.
- **Custom Rule Creation:** Allow users to define custom validation rules using the Pydantic models from the 'aws-resource-validator-drs' package. These custom rules could include additional constraints not covered by default validations.
- **Interactive CLI:** Develop an interactive command-line interface (CLI) for users to easily run validations, view reports, and manage their validation rules.
- **Integration with CI/CD:** Provide options to integrate 'DRSValidator' into CI/CD pipelines, ensuring that resource configurations are validated before deployment.

### Steps to Build the Application:
1. **Setup Project Environment:** Initialize a new Python project and install the necessary dependencies including 'aws-resource-validator-drs'.
2. **Define Validation Logic:** Use the Pydantic models from 'aws-resource-validator-drs' to define the validation logic for AWS DRS resources. This involves setting up validators to check various aspects of the resource configurations such as replication jobs, protection groups, etc.
3. **Implement Batch Processing:** Create a function that accepts a list of AWS DRS resources and validates each one in the batch. After processing, generate a comprehensive report detailing any issues found during the validation.
4. **Custom Rules Module:** Develop a module within 'DRSValidator' that allows users to create and apply custom validation rules. These rules should be able to extend or modify the default validation criteria.
5. **Develop CLI Interface:** Utilize a Python library like Click to build a user-friendly CLI that supports commands for validating resources, viewing reports, and managing custom rules.
6. **CI/CD Integration:** Document and provide examples on how to integrate 'DRSValidator' into existing CI/CD workflows, ensuring that it can be seamlessly included in automated testing processes.
7. **Testing and Documentation:** Write comprehensive tests for all functionalities and create detailed documentation to guide users through setup, usage, and customization of 'DRSValidator'.

By following these steps and incorporating the features mentioned, you will have developed a powerful and flexible tool for validating AWS DRS resources, leveraging the capabilities of the 'aws-resource-validator-drs' package.