1. Managing AI Data Pipeline Workflows with CloudFormation


    When managing AI data pipeline workflows with AWS CloudFormation, you use CloudFormation to define resources and configurations in a text file, usually JSON or YAML format. This file, known as a template, specifies the AWS resources that make up your stack, providing a simple and repeatable way to automate the provisioning and update of your AWS infrastructure.

    Pulumi, on the other hand, allows you to define infrastructure as code using familiar programming languages, such as Python, providing you with an expressive and programmable approach.

    Below is a Pulumi program in Python showing how you might define an AI data pipeline workflow. As a hypothetical example, let's say you need to process data with a serverless approach using AWS Lambda, store results in an S3 bucket, and manage it all with IAM roles and policies for access control.

    import pulumi import pulumi_aws as aws # Create an S3 bucket to store processed data data_bucket = aws.s3.Bucket("dataBucket") # Define an IAM role that the Lambda function will assume lambda_role = aws.iam.Role("lambdaRole", assume_role_policy=json.dumps({ "Version": "2012-10-17", "Statement": [{ "Action": "sts:AssumeRole", "Principal": { "Service": "lambda.amazonaws.com", }, "Effect": "Allow", "Sid": "", }] })) # Attach the policy to the role that grants permission to put objects in the bucket lambda_role_policy = aws.iam.RolePolicy("lambdaRolePolicy", role=lambda_role.id, policy=data_bucket.arn.apply(lambda arn: json.dumps({ "Version": "2012-10-17", "Statement": [{ "Action": ["s3:PutObject"], "Resource": [arn + "/*"], "Effect": "Allow", }] })) ) # Define the Lambda function that processes data data_processor = aws.lambda_.Function("dataProcessor", code=pulumi.FileArchive("./data_processor.zip"), # The zip archive of your Lambda function code and dependencies runtime="python3.8", # The runtime environment for the Lambda function role=lambda_role.arn, # The IAM role that the function will assume handler="data_processor.handler", # The function within your code that Lambda calls to begin execution ) # Grant the Lambda function permissions to read and write to the S3 bucket s3_permissions = aws.lambda_.Permission("s3Permissions", action="lambda:InvokeFunction", function=data_processor.arn, principal="s3.amazonaws.com", source_arn=data_bucket.arn, ) # Export the name of the bucket pulumi.export('bucket_name', data_bucket.id)

    In this program, we've declared resources using Pulumi's AWS package. We start by creating an S3 bucket, where we'll store the output of our data processing. We then create an IAM role with an attached policy that gives Lambda permission to put objects in our S3 bucket. After that, we define the Lambda function responsible for our data processing, and finally, we give this function permissions to be invoked by S3 events. At the end, we export the name of the S3 bucket so we can easily find it in the AWS console or when using the AWS CLI.

    Of course, this is a simplified example to illustrate the concept, and real-world applications may involve more complex policies, different AWS services like AWS Glue for ETL jobs, AWS Step Functions for workflow management, or Amazon SageMaker for machine learning jobs.

    By modeling your resources programmatically, Pulumi helps with complex deployment scenarios that require conditional logic, loops, building abstractions, sharing and reusing code, and accessing the full power of the chosen programming language and its ecosystem.