1. Fine-Grained Access Control for Data Analytics with AWS Cognito


    To achieve fine-grained access control for data analytics using AWS Cognito, you will likely need to use a combination of AWS Cognito User Pools, Identity Pools, and possibly other services such as AWS IAM for defining roles and permissions.

    Here’s an outline of the steps you’ll need to follow, which I’ll translate into a Pulumi Python program:

    1. Create a User Pool: This acts as a user directory and will handle user sign-up, sign-in, and associated user management tasks.
    2. Define an Identity Pool: This authorizes users from the User Pool to assume temporary AWS credentials, which will have permissions attached to allow for fine-grained access control.
    3. Define IAM Roles: You will have IAM roles representing different permission levels, which you will assign to authenticated and unauthenticated users from the Identity Pool.
    4. Role Mapping: In the Identity Pool, define role mappings that allow you to specify which IAM roles are assumed by users based on claims in their token.

    Let's set up a Pulumi program in Python that will create these resources and establish fine-grained access control.

    import pulumi import pulumi_aws as aws # Create an AWS Cognito User Pool user_pool = aws.cognito.UserPool("analyticsUserPool", name="AnalyticsUserPool", password_policy=aws.cognito.UserPoolPasswordPolicyArgs( minimum_length=8, require_numbers=True, require_symbols=True, require_lowercase=True, require_uppercase=True, ), auto_verified_attributes=["email"] ) # Create an AWS Cognito Identity Pool identity_pool = aws.cognito.IdentityPool("analyticsIdentityPool", identity_pool_name="AnalyticsIdentityPool", allow_unauthenticated_identities=False, # Security best practice. cognito_identity_providers=[aws.cognito.IdentityPoolCognitoIdentityProviderArgs( provider_name=user_pool.endpoint, client_id=app_client.id )] ) # Assume you have defined two IAM roles elsewhere in your Pulumi program: # `authenticated_role_arn` for authenticated users, and # `unauthenticated_role_arn` for guest users. # Attach roles to the Cognito Identity Pool # For the sake of example, assume `authenticated_role_arn` and `unauthenticated_role_arn` are available. identity_pool_role_attachment = aws.cognito.IdentityPoolRoleAttachment("analyticsIdentityPoolRoleAttachment", identity_pool_id=identity_pool.id, roles={ "authenticated": authenticated_role_arn, "unauthenticated": unauthenticated_role_arn, } ) # Output the User Pool and Identity Pool IDs for reference pulumi.export("user_pool_id", user_pool.id) pulumi.export("identity_pool_id", identity_pool.id)

    In this Pulumi program:

    • We create a UserPool, which is where all our users will reside.
    • We then create an IdentityPool which is used to federate user identities from the User Pool to AWS services.
    • We assume you have IAM roles already defined or you can define these within Pulumi. These roles will be associated with different levels of access to AWS services.
    • The IdentityPoolRoleAttachment resource is responsible for connecting these roles with the Identity Pool. This attachment tells AWS Cognito which IAM roles to use for authenticated (signed-in) and unauthenticated (guest) users.
    • Finally, pulumi.export provides the IDs of these resources.

    Please note that you should replace app_client.id with the actual ID of an app client that you've set up within the UserPool. The IAM roles represented by authenticated_role_arn and unauthenticated_role_arn should have permissions that control what AWS resources and actions users are allowed to access.

    For further information and to read more about the resources created in this program, refer to the following documentation:

    Remember, once the program is written, you will have to deploy it using the Pulumi CLI tools, and it assumes all necessary permissions and configurations are already set up on AWS and Pulumi.