1. Lambda Functions for Real-time Streaming Analysis


    To accomplish real-time streaming analysis with AWS Lambda, you would use AWS Lambda functions to process data as it arrives, typically from a streaming data source like Amazon Kinesis, DynamoDB Streams, or Amazon SQS. The Lambda function can be triggered by the incoming stream, process the data, and then output the results to another data store or system for further analysis or visualization.

    For this purpose, you need to create an AWS Lambda function using Pulumi. You will first need to define the function by providing the code that will execute whenever the function is invoked by the streaming data source. The Lambda function will be granted the necessary permissions and roles to allow it to access the streaming data source and any other AWS services it needs to interact with.

    Below is a Pulumi Python program that sets up a Lambda function for real-time streaming analysis:

    import pulumi import pulumi_aws as aws # Define the IAM role that will allow the Lambda function to run and access other AWS services. lambda_role = aws.iam.Role("lambdaRole", assume_role_policy='''{ "Version": "2012-10-17", "Statement": [{ "Action": "sts:AssumeRole", "Effect": "Allow", "Principal": { "Service": "lambda.amazonaws.com" } }] }''') # Attach the AWSLambdaBasicExecutionRole policy to the role created above so that # Lambda can write logs to CloudWatch. policy_attachment = aws.iam.RolePolicyAttachment("lambdaRoleAttachment", role=lambda_role.name, policy_arn="arn:aws:iam::aws:policy/service-role/AWSLambdaBasicExecutionRole") # Define the Lambda function. lambda_function = aws.lambda_.Function("streamProcessor", role=lambda_role.arn, handler="index.handler", runtime="python3.8", # The code for the Lambda function should be defined here. # For example, it can be a ZIP file containing your Lambda code. # For Python, the handler in the ZIP file should match the 'handler' property above. code=pulumi.FileArchive("./path_to/lambda_code.zip")) # The following is an example of setting up a trigger for the Lambda function. # This can be a Kinesis stream, DynamoDB stream, etc. # Here, 'my_stream' should be replaced by your actual stream's identifier. stream_event_source_mapping = aws.lambda_.EventSourceMapping("streamEventSourceMapping", event_source_arn="arn:aws:kinesis:us-west-2:123456789012:stream/my_stream", function=lambda_function.name) # Export the ARN of the Lambda function to access it later if necessary. pulumi.export("lambda_function_arn", lambda_function.arn)

    In this program, we:

    1. Create an IAM Role which the Lambda function will assume when it is executed. This role includes a trust policy that allows the Lambda service to assume the Role.
    2. Attach a standard AWS managed policy, AWSLambdaBasicExecutionRole, to the Role. This policy grants permissions for the Lambda function to log to CloudWatch, which is helpful for debugging and monitoring.
    3. Define the Lambda function by providing the necessary parameters like the role, handler, and runtime. The code property is where you would specify the location of your Lambda function's deployment package. The handler is the method in your code where Lambda begins execution.
    4. Optionally, set up an EventSourceMapping. This associates an event source, such as a Kinesis stream, with the Lambda function. Whenever the stream receives new data, AWS Lambda will invoke your function.

    Make sure to replace "./path_to/lambda_code.zip" with the actual path to your zipped code and "index.handler" with the correct handler inside your code. Additionally, ensure that the event_source_arn is the ARN of your Kinesis stream or another supported AWS streaming service.

    This program sets up the basic infrastructure needed to process real-time streams with AWS Lambda. You would need to adjust the code to fit the requirements of your specific streaming processing logic. The actual stream processing code would be contained within the Lambda deployment package referred to in this Pulumi program.