Validate Digital Twin Data Pipelines with Great Expectations and MLflow

"""
Production implementation for validating digital twin data pipelines using Great Expectations and MLflow.
Provides secure, scalable operations for data validation and processing.
"""

from typing import Dict, Any, List
import os
import logging
import time
import json
import mlflow
from great_expectations import DataContext

# Set up logging to capture important events in the application
logging.basicConfig(level=logging.INFO)
logger = logging.getLogger(__name__)

class Config:
    """
    Configuration class for environment variables.
    """
    database_url: str = os.getenv('DATABASE_URL')
    ge_path: str = os.getenv('GREAT_EXPECTATIONS_PATH')
    mlflow_tracking_uri: str = os.getenv('MLFLOW_TRACKING_URI')

    def __init__(self):
        if not self.database_url:
            raise ValueError("DATABASE_URL must be set")
        if not self.ge_path:
            raise ValueError("GREAT_EXPECTATIONS_PATH must be set")
        if not self.mlflow_tracking_uri:
            raise ValueError("MLFLOW_TRACKING_URI must be set")

# Initialize the configuration class
config = Config()

# Set MLflow tracking URI
mlflow.set_tracking_uri(config.mlflow_tracking_uri)

def validate_input_data(data: Dict[str, Any]) -> bool:
    """Validate request data for the pipeline.
    
    Args:
        data: Input data to validate
    Returns:
        True if valid
    Raises:
        ValueError: If validation fails
    """
    if 'id' not in data:
        raise ValueError('Missing id')
    if 'timestamp' not in data:
        raise ValueError('Missing timestamp')
    return True

def sanitize_fields(data: Dict[str, Any]) -> Dict[str, Any]:
    """Sanitize input data fields to prevent injection attacks.
    
    Args:
        data: Input data to sanitize
    Returns:
        Sanitized data
    """
    return {k: str(v).strip() for k, v in data.items()}

def normalize_data(data: Dict[str, Any]) -> Dict[str, Any]:
    """Normalize data fields for consistent processing.
    
    Args:
        data: Input data to normalize
    Returns:
        Normalized data
    """
    # Example normalization logic
    data['timestamp'] = pd.to_datetime(data['timestamp']).isoformat()
    return data

def transform_records(data: Dict[str, Any]) -> Dict[str, Any]:
    """Transform records for ML model input.
    
    Args:
        data: Input data to transform
    Returns:
        Transformed data
    """
    # Example transformation logic
    return {k: v for k, v in data.items() if k in ['id', 'features']}

# Fetch data from the source
def fetch_data() -> List[Dict[str, Any]]:
    """Fetch data from the database or an API.
    
    Returns:
        List of data records
    """
    # Placeholder for fetching logic
    return [{'id': '1', 'timestamp': '2023-01-01T00:00:00Z', 'features': {}}]

# Save processed data to the database
def save_to_db(data: Dict[str, Any]) -> None:
    """Save validated and processed data back to the database.
    
    Args:
        data: Data to save
    """
    # Placeholder for saving logic
    logger.info("Data saved to database")

# Aggregate metrics for monitoring
def aggregate_metrics(metrics: Dict[str, Any]) -> None:
    """Aggregate metrics for monitoring and logging.
    
    Args:
        metrics: Metrics to log
    """
    logger.info(f"Aggregated metrics: {metrics}")

# Main orchestrator class
class DataPipeline:
    """Main orchestrator for the data validation pipeline.
    """

    def __init__(self):
        self.ge_context = DataContext(config.ge_path)

    def run(self) -> None:
        """Run the full data validation and processing pipeline.
        """
        try:
            raw_data = fetch_data()  # Fetch data
            for record in raw_data:
                validate_input_data(record)  # Validate each record
                sanitized_record = sanitize_fields(record)  # Sanitize input
                normalized_record = normalize_data(sanitized_record)  # Normalize data
                transformed_record = transform_records(normalized_record)  # Transform data

                # Validate with Great Expectations
                self.ge_context.run_validation_operator("default", run_id="pipeline_run")

                save_to_db(transformed_record)  # Save processed data
                aggregate_metrics(transformed_record)  # Log metrics

        except ValueError as e:
            logger.error(f"Validation error: {e}")  # Log validation errors
        except Exception as e:
            logger.error(f"Unexpected error: {e}")  # Log unexpected errors
        finally:
            logger.info("Pipeline run completed")  # Indicate completion

if __name__ == '__main__':
    # Example usage of the data pipeline
    pipeline = DataPipeline()
    pipeline.run()  # Run the data validation pipeline