Coordinating Data Preparation Tasks with AWS Step Functions

Question

Pulumi · Accepted Answer

AWS Step Functions is a service that lets you coordinate the components of distributed applications and microservices using visual workflows. It’s well-suited for coordinating tasks that involve multiple AWS services such as data preparation, which may include activities like data extraction, transformation, and loading (ETL). AWS Step Functions ensures that these components execute in an order that you specify, can handle error handling, and supports complex workflows such as branching and parallel execution.

To illustrate how you can coordinate data preparation tasks using AWS Step Functions, let’s create a workflow that involves:

1. Triggering a Lambda function to extract data.
2. Using AWS Glue to transform the data.
3. Loading the transformed data into an Amazon S3 bucket.

Here's a detailed program written in Python using Pulumi to set up such a workflow:

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

# Create an IAM role that AWS Step Functions can assume.
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 the necessary policies to the role.
policy_arns = [
    "arn:aws:iam::aws:policy/service-role/AWSLambdaRole", # Policy for Lambda execution
    "arn:aws:iam::aws:policy/AWSStepFunctionsFullAccess", # Full access to Step Functions
    "arn:aws:iam::aws:policy/AmazonS3FullAccess",         # Full access to S3
    "arn:aws:iam::aws:policy/AWSGlueConsoleFullAccess"    # Full access to AWS Glue
]

for policy_arn in policy_arns:
    aws.iam.RolePolicyAttachment(f"{policy_arn.split('/')[-1]}Attachment",
                                 role=step_function_role.name,
                                 policy_arn=policy_arn)

# Define a Lambda function to simulate the data extraction.
data_extraction_lambda = aws.lambda_.Function("dataExtractionLambda",
    role=step_function_role.arn,
    handler="index.handler",
    runtime="python3.7",
    code=pulumi.FileArchive("./data_extraction_lambda") # The directory containing your Lambda code.
)

# Define an AWS Glue job for the data transformation process.
data_transformation_job = aws.glue.Job("dataTransformationJob",
    role_arn=step_function_role.arn,
    glue_version="2.0",
    command={
        "name": "glueetl",
        "scriptLocation": "s3://my-glue-scripts/data_transform.py" # The S3 path to your Glue script.
    }
)

# Define a Step Function state machine to coordinate the tasks.
step_function_definition = {
    "Comment": "A simple AWS Step Functions state machine that performs data preparation tasks.",
    "StartAt": "ExtractData",
    "States": {
        "ExtractData": {
            "Type": "Task",
            "Resource": f"arn:aws:lambda:{aws.get_region().name}:{aws.get_caller_identity().account_id}:function:{data_extraction_lambda.name}",
            "Next": "TransformData"
        },
        "TransformData": {
            "Type": "Task",
            "Resource": f"arn:aws:states:::glue:startJobRun.sync",
            "Parameters": {
                "JobName": data_transformation_job.name
            },
            "Next": "LoadData"
        },
        "LoadData": {
            "Type": "Task",
            "Resource": "arn:aws:states:::s3:putObject",
            "Parameters": {
                "Bucket": "my-data-bucket", # The S3 bucket where the transformed data will be stored.
                "Key": "transformed-data/output.csv",
                "Body.$": "$.result"       # The result of the Glue job, used as the body of the S3 object.
            },
            "End": True
        }
    }
}

# Create the Step Function state machine.
state_machine = aws.sfn.StateMachine("dataPreparationStateMachine",
    role_arn=step_function_role.arn,
    definition=json.dumps(step_function_definition)
)

# Export the ARN of the state machine so that it can be invoked.
pulumi.export('state_machine_arn', state_machine.arn)
```

This program does several things:
* Creates an IAM role for AWS Step Functions to assume and attaches the necessary policy ARNS for execution.
* Defines a Lambda function that mocks the data extraction phase, simulating the extraction of data from some source.
* Sets up an AWS Glue job to perform data transformation, assuming a script located in an S3 bucket (replace the script location with the actual path to your Glue script).
* Creates a state machine in AWS Step Functions with three states: data extraction (a Lambda function), data transformation (an AWS Glue job), and data loading (an S3 putObject task). Each state in this machine is a step in the data preparation process.
* Exports the ARN of the state machine, so you can invoke or manage it outside of Pulumi.

To use this program, you will need to provide your code in the specified locations, and you may need to adjust resource names and the state machine definition to fit your specific use case.

Remember, the key names (e.g., "ExtractData", "TransformData") in the definition of the state machine are used as identifiers in the visual workflow and the state transitions.

Before running this program:
- Ensure you have AWS CLI configured with necessary access rights.
- Replace placeholder paths with actual paths to your Lambda code and Glue script.
- Create the Lambda code package and the Glue script (if not already present in the S3 bucket).

Run the program using the `pulumi up` command, which will provision the necessary resources and output the ARN of the State Machine that you've created.