1. Fine-grained Access for Lambda-based Data Transformation Jobs


    To implement fine-grained access for Lambda-based data transformation jobs, you'll need to set up a few things in AWS. You'll interact mainly with AWS Identity and Access Management (IAM) and AWS Lambda.

    Firstly, you will need an IAM Role that your Lambda function can assume. This role will have the permissions required for the AWS services that your Lambda interacts with, such as reading from source data stores or writing to target data stores.

    Next, you can also create an IAM Policy with restricted permissions and attach it to the role to provide fine-grained access control. For example, if your Lambda reads from a specific S3 bucket and writes to an AWS RDS instance, you can create a policy that allows only the required actions on these specific resources.

    Finally, you will define a Lambda function, using the role you created, to perform the data transformation job. The function's code, which is not part of the infrastructure code, would be responsible for the actual transformation.

    Let's write a Pulumi program in Python that sets up these resources:

    import pulumi import pulumi_aws as aws # Create an IAM Role for the Lambda function lambda_execution_role = aws.iam.Role("lambdaExecutionRole", assume_role_policy="""{ "Version": "2012-10-17", "Statement": [{ "Action": "sts:AssumeRole", "Effect": "Allow", "Principal": { "Service": "lambda.amazonaws.com" } }] }""") # Attach the AWS-managed policy for Lambda execution to the role aws_lambda_basic_execution = aws.iam.RolePolicyAttachment("awsLambdaBasicExecution", role=lambda_execution_role.name, policy_arn=aws.iam.ManagedPolicy.AWS_LAMBDA_BASIC_EXECUTION_ROLE) # Create an IAM Policy that allows specific actions and attach it to the role lambda_policy = aws.iam.Policy("lambdaPolicy", policy="""{ "Version": "2012-10-17", "Statement": [{ "Effect": "Allow", "Action": [ "rds:BatchExecuteStatement", "s3:GetObject" ], "Resource": [ "arn:aws:rds:{region}:{account}:db:{db_name}", "arn:aws:s3:::{bucket_name}/*" ] }] }""") lambda_policy_attachment = aws.iam.RolePolicyAttachment("lambdaPolicyAttachment", role=lambda_execution_role.name, policy_arn=lambda_policy.arn) # Define the Lambda function with the role and a sample code lambda_function = aws.lambda_.Function("dataTransformationJob", role=lambda_execution_role.arn, runtime="python3.8", handler="index.handler", code=pulumi.AssetArchive({ '.': pulumi.FileArchive('./path_to_lambda_deployment_package') }), timeout=60) # Export the Lambda ARN so it can be easily retrieved pulumi.export('lambda_arn', lambda_function.arn)

    Here is an explanation of the program:

    • We create an IAM role lambdaExecutionRole that our Lambda function will assume. This role has an assume_role_policy that allows the Lambda service to assume this role.
    • We attach the Lambda basic execution role policy to our custom role, which allows the Lambda function to log to CloudWatch and execute.
    • We then create a custom IAM policy lambda_policy that defines fine-grained permissions—for instance, GetObject on a specific S3 bucket and BatchExecuteStatement on an RDS instance. This policy is attached to the lambdaExecutionRole with lambda_policy_attachment.
    • The lambda_function is defined with the runtime and handler specified, and with an archive of the code to run.
    • Finally, we export the ARN of the Lambda function to make it accessible outside of Pulumi for other applications or for reference.

    Make sure to replace placeholders like {region}, {account}, {db_name}, and {bucket_name} with your actual AWS region, account ID, database name, and S3 bucket name. The IAM policy should be tailored precisely to grant the minimum necessary permissions that your Lambda needs to perform its job.

    The path_to_lambda_deployment_package is the directory where your Lambda function's deployment package is stored. Replace it with the actual path to your Lambda code.

    This program is a starting point. You may need to adapt it to the specific needs of your data transformation jobs, such as adding additional permissions to the custom IAM policy or configuring environment variables for your Lambda function.