1. Building a smart city analytics platform using AWS IoT Greengrass, Lambda for processing, and visualization in Grafana


    Based on your request, we will need several resources. For example, we'll need:

    1. AWS IoT Greengrass components for device connectivity and messaging.
    2. AWS Lambda to run the code that processes the IoT data.
    3. AWS Managed Grafana workspace for data visualization.

    Please note that Pulumi currently doesn't support AWS IoT Greengrass components directly, but it does allow for other ways of reaching the same goal. I'll assume that we are not bound by the use of "Greengrass" strictly and similar functionality can be used.

    I'll illustrate how to define a simple lambda function, a Grafana workspace, and how an IoT core thing could trigger an AWS Lambda function. We will skip actual data processing and visualization as they highly depend on your specific use case.

    Below is a simple example of a Pulumi program written in Python.

    import pulumi from pulumi_aws import lambda_, iot, iam, grafana lambda_role = iam.Role("lambdaRole", assume_role_policy="""{ "Version": "2012-10-17", "Statement": [ { "Action": "sts:AssumeRole", "Principal": { "Service": "lambda.amazonaws.com" }, "Effect": "Allow", "Sid": "" } ] }""", ) iot_role = iam.Role('iotRole', assume_role_policy = """{ "Version": "2012-10-17", "Statement": [ { "Action": "sts:AssumeRole", "Principal" : { "Service" : "iot.amazonaws.com" }, "Effect": "Allow", "Sid": "" } ] }""" ) role_policy_attachment = iam.RolePolicyAttachment('lambdaFullAccess', role=lambda_role.id, policy_arn='arn:aws:iam::aws:policy/AWSLambdaFullAccess' ) iot_thing = iot.Thing('iotThing') iot_role_alias = iot.RoleAlias('iotRoleAlias', alias='alias', role_arn=iot_role.arn ) iot_policy = iot.Policy('iotPolicy', policy="""{ "Version": "2012-10-17", "Statement": [ { "Effect": "Allow", "Action": "iot:Publish", "Resource": "*" } ] }""" ) iot_principal_policy = iot.PolicyPrincipalAttachment('iotPrincipalPolicy', policy_name=iot_policy.name, principal=iot_role_alias.arn ) iot_thing_principal = iot.ThingPrincipalAttachment('iotThingPrincipal', thing=iot_thing.name, principal=iot_role_alias.arn ) lambda_function = lambda_.Function("lambdaFunction", code=pulumi.AssetArchive({ '.': pulumi.FileArchive('./path_to_your_python_script_folder') }), role=lambda_role.arn, handler="your_python_script.handler", runtime="python3.8" ) grafana_workspace = grafana.Workspace('grafanaWorkspace', account_access_type='ORGANIZATION', authentication_providers=['AWS_SSO'], configuration='string', permission_type='SERVICE_MANAGED' ) pulumi.export('iot_thing_arn', iot_thing.arn) pulumi.export('lambda_function_arn', lambda_function.arn) pulumi.export('grafana_workspace_id', grafana_workspace.id)

    Before you run the program, replace './path_to_your_python_script_folder' with the actual path to your Lambda function code, and "your_python_script.handler" with the entry point to your Python script. Also, remember to replace 'string' with the actual configuration string for your Grafana workspace.

    The program created:

    1. An AWS IoT Thing and allowed it to publish messages.

    2. An AWS Lambda function.

    3. A Grafana workspace for visualization.

    Some parts of AWS data pass architecture are missing here, like AWS IoT Topic rule to trigger Lambda, defining the Lambda function to process the data in a specific way, and configuring Grafana dashboard. This is a significant project with many specific project-based requirements.

    For more information on the Pulumi resources used in this program, refer to the Pulumi Registry: