Automate Pipeline Workflows with ZenML and Azure Digital Twins SDK

                      
                     
"""
Production implementation for automating pipeline workflows using ZenML and Azure Digital Twins SDK.
Provides secure, scalable operations to integrate and process digital twin data efficiently.
"""

from typing import Dict, Any, List
import os
import logging
import time
import json
from azure.digitaltwins import DigitalTwinsClient
from azure.identity import DefaultAzureCredential

# Set up logging
logging.basicConfig(level=logging.INFO)
logger = logging.getLogger(__name__)

class Config:
    """
    Configuration class for environment variables.
    """
    def __init__(self):
        self.azure_twin_url: str = os.getenv('AZURE_TWIN_URL')
        self.zenml_repo_url: str = os.getenv('ZENML_REPO_URL')

config = Config()

async def validate_input(data: Dict[str, Any]) -> bool:
    """Validate input data for pipeline processing.

    Args:
        data: Input dictionary containing pipeline parameters.
    Returns:
        True if valid, raises ValueError otherwise.
    Raises:
        ValueError: If validation fails.
    """
    if 'pipeline_id' not in data:
        raise ValueError('Missing pipeline_id in input data.')
    return True

async def sanitize_fields(data: Dict[str, Any]) -> Dict[str, Any]:
    """Sanitize input fields to prevent security issues.

    Args:
        data: Raw input data dictionary.
    Returns:
        Cleaned input data dictionary.
    """
    # Here we would sanitize the fields to remove any harmful input
    return {k: str(v).strip() for k, v in data.items()}

async def fetch_data(twin_id: str) -> Dict[str, Any]:
    """Fetch data from Azure Digital Twins.

    Args:
        twin_id: Unique identifier for the digital twin.
    Returns:
        Dictionary representation of the digital twin data.
    Raises:
        Exception: If data fetching fails.
    """
    try:
        credential = DefaultAzureCredential()
        client = DigitalTwinsClient(config.azure_twin_url, credential)
        twin_data = client.get_digital_twin(twin_id)
        return json.loads(twin_data)
    except Exception as e:
        logger.error(f'Error fetching data: {str(e)}')
        raise

async def transform_records(data: List[Dict[str, Any]]) -> List[Dict[str, Any]]:
    """Transform raw records into a structured format.

    Args:
        data: List of raw records from Azure.
    Returns:
        List of structured records after transformation.
    """
    # Example transformation logic
    return [{'id': record['id'], 'value': record['value']} for record in data]

async def process_batch(records: List[Dict[str, Any]]) -> None:
    """Process a batch of records and save results.

    Args:
        records: List of records to process.
    """
    for record in records:
        logger.info(f'Processing record: {record}')
        # Imagine saving to a database or further processing

async def aggregate_metrics(records: List[Dict[str, Any]]) -> Dict[str, Any]:
    """Aggregate metrics from processed records.

    Args:
        records: List of processed records.
    Returns:
        Dictionary containing aggregated metrics.
    """
    # Sample aggregation logic
    return {'total_records': len(records)}

async def save_to_db(data: Dict[str, Any]) -> None:
    """Save processed data to a database.

    Args:
        data: Data to be saved.
    """
    # Placeholder for database save logic
    logger.info(f'Saving data to database: {data}')

async def handle_errors(error: Exception) -> None:
    """Handle errors that occur during processing.

    Args:
        error: Exception raised during processing.
    """
    logger.error(f'Handling error: {str(error)}')

class PipelineOrchestrator:
    """Main orchestrator for pipeline execution.
    """
    async def execute_pipeline(self, data: Dict[str, Any]) -> None:
        """Execute the entire pipeline workflow.

        Args:
            data: Input data for the pipeline.
        """
        try:
            await validate_input(data)
            sanitized_data = await sanitize_fields(data)
            twin_data = await fetch_data(sanitized_data['twin_id'])
            transformed_records = await transform_records(twin_data)
            await process_batch(transformed_records)
            metrics = await aggregate_metrics(transformed_records)
            await save_to_db(metrics)
        except Exception as e:
            await handle_errors(e)

if __name__ == '__main__':
    # Example usage
    import asyncio
    example_data = {'pipeline_id': '12345', 'twin_id': 'twin_id_example'}
    orchestrator = PipelineOrchestrator()
    asyncio.run(orchestrator.execute_pipeline(example_data))