Validate Manufacturing Data Pipelines with Great Expectations and DVC

                      
                     
"""
Production implementation for validating manufacturing data pipelines using Great Expectations and DVC.
Provides secure, scalable operations while ensuring data quality and integrity.
"""
from typing import Dict, Any, List
import os
import logging
import great_expectations as ge
import pandas as pd
from dvc.api import Repo
import requests

# Logging configuration for tracking application actions
logging.basicConfig(level=logging.INFO)
logger = logging.getLogger(__name__)

class Config:
    """
    Configuration class to handle environment variables.
    """
    database_url: str = os.getenv('DATABASE_URL')
    great_expectations_path: str = os.getenv('GREAT_EXPECTATIONS_PATH')
    dvc_repo_url: str = os.getenv('DVC_REPO_URL')

async def validate_input(data: Dict[str, Any]) -> bool:
    """Validate input data for required fields.
    
    Args:
        data: Input data to validate
    Returns:
        bool: True if valid
    Raises:
        ValueError: If validation fails
    """
    if 'manufacturing_id' not in data:
        raise ValueError('Missing manufacturing_id')  # Ensure key is present
    return True  # Validation passed

async def sanitize_fields(data: Dict[str, Any]) -> Dict[str, Any]:
    """Sanitize input fields for security.
    
    Args:
        data: Input data to sanitize
    Returns:
        Dict[str, Any]: Sanitized data
    """
    # Remove any unwanted fields
    sanitized_data = {k: v for k, v in data.items() if k in ['manufacturing_id', 'measurement', 'timestamp']}
    return sanitized_data

async def fetch_data(manufacturing_id: str) -> Dict[str, Any]:
    """Fetch manufacturing data from an external API.
    
    Args:
        manufacturing_id: The ID of the manufacturing record
    Returns:
        Dict[str, Any]: Retrieved data
    Raises:
        ConnectionError: If the API call fails
    """
    url = f'https://api.example.com/manufacturing/{manufacturing_id}'
    response = requests.get(url)
    if response.status_code != 200:
        raise ConnectionError(f'Failed to fetch data: {response.status_code}')  # Handle connection errors
    return response.json()  # Return fetched data

async def transform_records(data: Dict[str, Any]) -> Dict[str, Any]:
    """Transform records to the expected structure.
    
    Args:
        data: Raw data to transform
    Returns:
        Dict[str, Any]: Transformed data
    """
    # Convert timestamps to datetime objects, for example
    data['timestamp'] = pd.to_datetime(data['timestamp'])
    return data  # Return transformed data

async def process_batch(data_batch: List[Dict[str, Any]]) -> None:
    """Process a batch of manufacturing data.
    
    Args:
        data_batch: List of data records to process
    Raises:
        Exception: If processing fails
    """
    for data in data_batch:
        try:
            await validate_input(data)  # Validate each record
            sanitized_data = await sanitize_fields(data)  # Sanitize the record
            transformed_data = await transform_records(sanitized_data)  # Transform the record
            # Integrate with Great Expectations for validation
            context = ge.data_context.DataContext(Config.great_expectations_path)
            suite = context.get_expectation_suite('manufacturing_suite')
            batch = ge.batch.Batch(data=transformed_data, expectation_suite=suite)
            context.run_validation_operator('action_list_operator', assets_to_validate=[batch])
            # Log successful processing
            logger.info(f'Successfully processed data for ID: {data["manufacturing_id"]}')
        except Exception as e:
            logger.error(f'Error processing data for ID: {data["manufacturing_id"]} - {str(e)}')  # Log errors

async def save_to_db(data: Dict[str, Any]) -> None:
    """Save validated data to the database.
    
    Args:
        data: Data to save
    Raises:
        Exception: If saving fails
    """
    # Simulated database save logic
    logger.info(f'Saving data to database: {data}')
    # Here you would implement actual database saving logic

async def main(manufacturing_ids: List[str]) -> None:
    """Main orchestration function for processing manufacturing data.
    
    Args:
        manufacturing_ids: List of manufacturing IDs to process
    """
    for manufacturing_id in manufacturing_ids:
        try:
            data = await fetch_data(manufacturing_id)  # Fetch data for each ID
            await process_batch([data])  # Process the fetched data
            await save_to_db(data)  # Save the data to the database
        except Exception as e:
            logger.error(f'Failed to process ID {manufacturing_id}: {str(e)}')  # Handle fetch/process errors

if __name__ == '__main__':
    # Example usage
    manufacturing_ids = ['123', '456', '789']  # Sample IDs to process
    import asyncio
    asyncio.run(main(manufacturing_ids))  # Run the main function in an event loop