Orchestrate Industrial Data and Model Assets with Full Lineage Using Dagster and MLflow

"""
Production implementation for orchestrating industrial data and model assets with full lineage using Dagster and MLflow.
Provides secure, scalable operations with logging, validation, and error handling.
"""
import os
import logging
from typing import Dict, Any, List, Union
from dagster import asset, job, op, Out, Output
from mlflow import log_metric, log_param, start_run, end_run

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

class Config:
    database_url: str = os.getenv('DATABASE_URL')
    mlflow_tracking_uri: str = os.getenv('MLFLOW_TRACKING_URI')

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')  # Ensure 'id' is present
    if not isinstance(data['id'], int):
        raise ValueError('id must be an integer')  # Validate id type
    return True  # Validation successful

def sanitize_fields(data: Dict[str, Any]) -> Dict[str, Any]:
    """Sanitize input fields.
    
    Args:
        data: Input data to sanitize
    Returns:
        Sanitized data
    """
    return {key: str(value).strip() for key, value in data.items()}  # Strip whitespace from all fields

@op(out=Out(dagster_type=Union[Dict[str, Any], None]))
def fetch_data() -> Dict[str, Any]:
    """Fetch data from the source.
    
    Returns:
        Fetched data
    """
    logger.info('Fetching data...')
    # Simulate data fetch
    data = {'id': 1, 'value': ' 1234 '}  # Example data
    return sanitize_fields(data)  # Sanitize before returning

def normalize_data(data: Dict[str, Any]) -> Dict[str, Any]:
    """Normalize data fields for processing.
    
    Args:
        data: Input data to normalize
    Returns:
        Normalized data
    """
    return {key: value.lower() for key, value in data.items()}  # Normalize to lower case

@op
def process_batch(data: Dict[str, Any]) -> None:
    """Process a batch of data.
    
    Args:
        data: Data to process
    """
    logger.info(f'Processing data: {data}')
    # Simulate processing
    if 'value' in data:
        processed_value = int(data['value']) + 100  # Example processing
        logger.info(f'Processed value: {processed_value}')
    else:
        logger.warning('No value found for processing')  # Warning for missing value

@op
def aggregate_metrics(data: List[Dict[str, Any]]) -> None:
    """Aggregate metrics from processed data.
    
    Args:
        data: List of processed data records
    """
    total = sum(int(record['value']) for record in data if 'value' in record)  # Aggregate values
    logger.info(f'Total aggregated value: {total}')  # Log aggregated result

@job
def data_pipeline():
    """Main data pipeline job.
    
    This orchestrates the complete workflow from fetching to processing data.
    """
    data = fetch_data()  # Fetch data
    validate_input(data)  # Validate input data
    normalized_data = normalize_data(data)  # Normalize data
    process_batch(normalized_data)  # Process data
    aggregate_metrics([normalized_data])  # Aggregate metrics

if __name__ == '__main__':
    # Execute the data pipeline
    with start_run() as run:
        logger.info('Starting MLflow run...')
        data_pipeline()  # Run pipeline
        log_param('source', 'data_source')  # Log parameters
        log_metric('records_processed', 1)  # Log metrics
        logger.info('Data pipeline completed successfully.')  # Completion log
        end_run()  # End the run