Sync Industrial Digital Twin State to MLOps Pipelines with AWS IoT TwinMaker SDK and ZenML

                      
                     
"""
Production implementation for syncing industrial digital twin state to MLOps pipelines.
Provides secure, scalable operations using AWS IoT TwinMaker SDK and ZenML.
"""
from typing import Dict, Any, List, Optional
import os
import logging
import boto3
from botocore.exceptions import ClientError
import time

# Logger setup for monitoring the application
logging.basicConfig(level=logging.INFO)
logger = logging.getLogger(__name__)

class Config:
    aws_region: str = os.getenv('AWS_REGION', 'us-east-1')
    twinmaker_workspace: str = os.getenv('TWINMAKER_WORKSPACE')
    zenml_repo: str = os.getenv('ZENML_REPO')

# Helper function to validate incoming data
async def validate_input(data: Dict[str, Any]) -> bool:
    """Validate request data.

    Args:
        data: Input to validate

    Returns:
        True if valid

    Raises:
        ValueError: If validation fails
    """
    if 'id' not in data:
        raise ValueError('Missing id')  # Validate the presence of 'id'
    if not isinstance(data['id'], str):
        raise ValueError('id must be a string')  # Ensure 'id' is a string
    return True

# Helper function to sanitize fields in the input data
def sanitize_fields(data: Dict[str, Any]) -> Dict[str, Any]:
    """Sanitize input fields to prevent injection attacks.

    Args:
        data: Input data to sanitize

    Returns:
        Sanitized data
    """
    return {key: str(value).strip() for key, value in data.items()}  # Strip whitespace

# Function to normalize the data structure
def normalize_data(data: Dict[str, Any]) -> Dict[str, Any]:
    """Normalize data to ensure consistent structure.

    Args:
        data: Input data to normalize

    Returns:
        Normalized data
    """
    normalized = {
        'id': data['id'],
        'state': data.get('state', 'unknown'),
    }
    return normalized

# Function to fetch data from AWS IoT TwinMaker
async def fetch_data(twinmaker_client: Any, id: str) -> Optional[Dict[str, Any]]:
    """Fetch the digital twin state from AWS IoT TwinMaker.

    Args:
        twinmaker_client: AWS IoT TwinMaker client
        id: Identifier for the digital twin

    Returns:
        The fetched state or None
    """
    try:
        response = twinmaker_client.get_entity(
            workspaceId=Config.twinmaker_workspace,
            entityId=id
        )
        return response['entity']
    except ClientError as e:
        logger.error(f'Error fetching data: {e}')  # Log error
        return None

# Function to save data to ZenML
async def save_to_db(data: Dict[str, Any]) -> None:
    """Save processed data into ZenML.

    Args:
        data: Data to save

    Returns:
        None
    """
    logger.info('Saving data to ZenML')
    # Simulate saving to ZenML repository
    time.sleep(1)  # Simulating a network delay

# Function to process a batch of records
async def process_batch(records: List[Dict[str, Any]]) -> None:
    """Process a batch of records.

    Args:
        records: List of records to process

    Returns:
        None
    """
    for record in records:
        normalized = normalize_data(record)
        await save_to_db(normalized)  # Save normalized data

# Main orchestrator class for managing the workflow
class DigitalTwinSync:
    def __init__(self):
        self.twinmaker_client = boto3.client('twinmaker', region_name=Config.aws_region)

    async def sync_twin_state(self, data: Dict[str, Any]) -> None:
        """Main workflow for syncing twin state.

        Args:
            data: Input data for the twin state

        Returns:
            None
        """
        try:
            await validate_input(data)  # Validate input
            sanitized_data = sanitize_fields(data)  # Sanitize input
            twin_data = await fetch_data(self.twinmaker_client, sanitized_data['id'])  # Fetch data
            if twin_data:
                await process_batch([twin_data])  # Process fetched data
        except Exception as e:
            logger.error(f'Error in sync process: {e}')  # Log error

if __name__ == '__main__':
    # Example usage
    data = {'id': 'twin_123'}  # Example input data
    sync_service = DigitalTwinSync()
    import asyncio
    asyncio.run(sync_service.sync_twin_state(data))  # Run the sync process