Makefile IaC

Creating a full solution with Terraform, Python, and a Makefile for automating environment creation, infrastructure provisioning, 
and test execution is quite an extensive task. However, I'll outline the components you'll need and provide examples to get started. 
This setup will include:
a) Terraform scripts for provisioning the AWS infrastructure (Kinesis Stream, Kinesis Data Firehose, S3 bucket, Lambda function, IAM roles).
b) Python script for the Lambda function.
c) Makefile for automating environment setup and testing.
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Step 1: AWS Infrastructure with Terraform
Create Terraform scripts to provision the necessary AWS resources. 

main.tf:

provider "aws" {
  region = "our-region"
}

resource "aws_kinesis_stream" "example_stream" {
  name             = "example-stream"
  shard_count      = 1
}

resource "aws_s3_bucket" "example_bucket" {
  bucket = "example-bucket"
}

resource "aws_iam_role" "lambda_execution_role" {
  name = "lambda_execution_role"

  assume_role_policy = jsonencode({
    Version = "2012-10-17"
    Statement = [
      {
        Action = "sts:AssumeRole"
        Effect = "Allow"
        Principal = {
          Service = "lambda.amazonaws.com"
        }
      },
    ]
  })
}

resource "aws_lambda_function" "example_lambda" {
  function_name = "exampleFunction"
  role          = aws_iam_role.lambda_execution_role.arn
  handler       = "index.handler"
  runtime       = "python3.8"

  # Assuming we have the Lambda function code in lambda_function.zip
  filename         = "lambda_function.zip"
  source_code_hash = filebase64sha256("lambda_function.zip")
}

# Add policies to the IAM role as needed, for example, allowing Lambda to write to CloudWatch Logs.
variables.tf: Define any variables used in your Terraform scripts.

outputs.tf: Define outputs from our Terraform scripts, such as the ARNs of created resources.
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Step 2: Python Lambda Function
Refer to the Python script in the code section for the Lambda function. 
Ensure we package this script into a deployment package (ZIP file) for Terraform to deploy.
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Step 3: Makefile (refrenced from source: https://developer.hashicorp.com/terraform/cli/run)
A Makefile to automate setup and testing might include:

init:
    terraform init

apply:
    terraform apply

destroy:
    terraform destroy

deploy_lambda:
    zip -r lambda_function.zip .
    aws lambda update-function-code --function-name exampleFunction --zip-file fileb://lambda_function.zip

test:
    # Add test commands here. This could involve invoking the Lambda function directly,
    # or putting records into the Kinesis stream to trigger it.
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Getting Started with procedures:(refrenced from source: https://developer.hashicorp.com/terraform/)

a) Terraform Initialization: Run make init to initialize Terraform.
b) Apply Infrastructure: Use make apply to provision the AWS resources.
c) Deploy Lambda: After making changes to  Lambda function, use make deploy_lambda to update the function code in AWS.
d) Testing: Implement the tests and run them with make test.
e) Cleanup: Use make destroy to tear down the infrastructure.
This outline should get us started, but we'll likely need to customize and extend it based on your project's specific requirements.
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This setup will automate environment creation and facilitate test execution for the 
data processing solution that enriches events from an Amazon Kinesis stream before storing them in an S3 data lake.

data-lake-project/
├── Makefile
├── terraform/
│   ├── main.tf
│   ├── variables.tf
│   └── outputs.tf
└── src/
    └── lambda_function.py

1. Terraform Configuration
The Terraform setup will provision an AWS Kinesis Stream, an AWS Lambda function for data transformation, and an S3 bucket for the data lake.
main.tf : This file defines the resources needed for the project.

provider "aws" {
  region = "our-region"
}

resource "aws_kinesis_stream" "example" {
  name             = "example-kinesis-stream"
  shard_count      = 1
}

resource "aws_s3_bucket" "data_lake" {
  bucket = "our-data-lake-bucket-name"
}

resource "aws_iam_role" "lambda_execution_role" {
  name = "lambda_execution_role"

  assume_role_policy = jsonencode({
    Version = "2012-10-17"
    Statement = [{
      Action = "sts:AssumeRole"
      Effect = "Allow"
      Principal = {
        Service = "lambda.amazonaws.com"
      }
    }]
  })
}

resource "aws_lambda_function" "data_enrichment" {
  function_name = "DataEnrichmentFunction"

  s3_bucket = "our-deployment-package-bucket"
  s3_key    = "lambda_function.zip"

  handler = "lambda_function.lambda_handler"
  runtime = "python3.8"
  role    = aws_iam_role.lambda_execution_role.arn
}

# Add necessary permissions for Lambda to access Kinesis and S3

variables.tf: Define any variables for our Terraform configuration here.
outputs.tf: Specify outputs from our Terraform configuration, such as ARNs and IDs that might be useful.

2. Makefile: The Makefile will automate the setup of the environment and the execution of tests.

init:
    cd terraform && terraform init

apply:
    cd terraform && terraform apply

destroy:
    cd terraform && terraform destroy

test:
    python3 -m unittest discover -s src/tests

.PHONY: init apply destroy test

Running the Automation
a) Initialize Terraform: Run make init to initialize Terraform.
b) Apply Terraform Configuration: Run make apply to provision the AWS infrastructure.
c) Destroy Infrastructure: When done, run make destroy to clean up resources to avoid unnecessary charges.
d) Execute Tests: Run make test to execute any Python tests  for our Lambda function .
e) Make sure to adjust the Terraform scripts and Python code according to your AWS setup and specific requirements, such as the S3 bucket name, 
IAM roles, and policies. This setup provides a foundational structure to automate the deployment and testing of our data lake project.
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