Automated Data Preprocessing Workflow with AWS Step Functions

Question

Pulumi · Accepted Answer

AWS Step Functions is a managed service provided by AWS that makes it easy to sequence and execute a series of AWS Lambda functions and other AWS services. Step Functions manages the operations and underlying infrastructure for you to ensure your application is available at any scale.

In the context of data preprocessing, we can build a workflow with Step Functions that orchestrates various AWS services to process data in a reliable, scalable, and maintainable manner. For instance, you could have AWS Lambda functions that clean and transform data, Amazon S3 for storing intermediate and final datasets, and Amazon SageMaker to conduct data preprocessing tasks using built-in algorithms or custom code.

In this program, I'll create a basic Step Functions state machine that could, conceptually, be part of an automated data preprocessing workflow. Here's an overview of how this might work:

1. An initial Lambda function is triggered (manually or by another AWS service event) that starts the Step Functions state machine.
2. The state machine contains several states: 
   * It starts with a Lambda function that retrieves raw data and performs initial processing.
   * It then uses a SageMaker job to perform more sophisticated preprocessing, like feature engineering, scaling, normalization, etc.
   * Finally, another Lambda function stores the processed data back in S3 or passes it to another service (like a machine learning training job).

For simplicity, I'll implement the Step Functions state machine and the Lambda functions that could be involved in the steps I described. In an actual deployment, you'd have to implement the logic within those Lambda functions to handle your specific preprocessing tasks and also tie in any necessary SageMaker processing jobs.

Let's write the program:

```python
import pulumi
import pulumi_aws as aws
import json

# Define an IAM role for the Step Function
# See: https://www.pulumi.com/registry/packages/aws/api-docs/iam/role/
step_function_role = aws.iam.Role("stepFunctionRole",
    assume_role_policy=json.dumps({
        "Version": "2012-10-17",
        "Statement": [{
            "Action": "sts:AssumeRole",
            "Effect": "Allow",
            "Principal": {
                "Service": "states.amazonaws.com"
            },
        }]
    })
)

# Attach necessary policies to the role for Lambda and Log access
# See: https://www.pulumi.com/registry/packages/aws/api-docs/iam/rolepolicyattachment/
aws.iam.RolePolicyAttachment("lambdaAccess",
    role=step_function_role.name,
    policy_arn=aws.iam.ManagedPolicy.AWS_LAMBDA_BASIC_EXECUTION_ROLE.value
)

# Create the first Lambda function that initiates our data processing
# See: https://www.pulumi.com/registry/packages/aws/api-docs/lambda/function/
preprocessing_lambda = aws.lambda_.Function("preprocessingLambda",
    role=step_function_role.arn,
    handler="index.handler",
    runtime=aws.lambda_.Runtime.PYTHON3_8,
    code=pulumi.AssetArchive({
        '.': pulumi.FileArchive('./preprocessing')
    })
)

# Define the state machine
# See: https://www.pulumi.com/registry/packages/aws/api-docs/stepfunctions/statemachine/
state_machine_definition = json.dumps({
    "Comment": "A simple AWS Step Functions state machine that automates data preprocessing",
    "StartAt": "PreprocessingData",
    "States": {
        "PreprocessingData": {
            "Type": "Task",
            "Resource": f"arn:aws:lambda:{aws.config.region}:{aws.config.account_id}:function:{preprocessing_lambda.name}",
            "End": True
        }
    }
})

# Define the state machine
state_machine = aws.sfn.StateMachine("dataPreprocessingStateMachine",
    role_arn=step_function_role.arn,
    definition=state_machine_definition,
)

# Export the state machine ARN, which you can use to start a run manually
pulumi.export('state_machine_arn', state_machine.id)
```

In the above program:

- A new IAM Role (`stepFunctionRole`) is created for the Step Functions State Machine. This role allows Step Functions to call AWS services on your behalf.
- A Lambda function (`preprocessingLambda`) is defined and will contain the logic for data preprocessing. The location of the code for this function is specified by the `code` parameter and should be a directory containing your Lambda's code.
- The Step Function State Machine (`dataPreprocessingStateMachine`) is defined with a single task state that invokes the Lambda function for data preprocessing. The state machine's definition is a JSON document that outlines the steps to be taken and the order to take them in.

Please ensure that you have the AWS Pulumi plugin installed and configured with appropriate AWS credentials before running this code. Also, ensure that `index.handler` in the Lambda function code points to the correct module and handler function in your code source, and that the `./preprocessing` directory exists and contains your function's code.