Transform Manufacturing Analytics Pipelines with dbt and DuckDB

                      
                     
"""
Production implementation for Transforming Manufacturing Analytics Pipelines with dbt and DuckDB.
Provides secure, scalable operations.
"""

from typing import Dict, Any, List, Tuple
import os
import logging
import duckdb
import pandas as pd
import time

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

class Config:
    database_url: str = os.getenv('DATABASE_URL', 'duckdb:///:memory:')
    retry_attempts: int = int(os.getenv('RETRY_ATTEMPTS', '5'))
    retry_delay: float = float(os.getenv('RETRY_DELAY', '2.0'))  # seconds

def validate_input(data: Dict[str, Any]) -> bool:
    """Validate input data for the pipeline.
    Args:
        data: Input data to validate
    Returns:
        True if valid
    Raises:
        ValueError: If validation fails
    """
    if 'manufacturing_data' not in data:
        raise ValueError('Missing required key: manufacturing_data')
    if not isinstance(data['manufacturing_data'], list):
        raise ValueError('manufacturing_data must be a list')
    return True

def sanitize_fields(data: List[Dict[str, Any]]) -> List[Dict[str, Any]]:
    """Sanitize fields in manufacturing data.
    Args:
        data: List of manufacturing records
    Returns:
        Sanitized list of records
    """
    # Example sanitization process
    for record in data:
        record['id'] = str(record['id']).strip()  # Sanitize ID
    return data

def normalize_data(data: List[Dict[str, Any]]) -> pd.DataFrame:
    """Normalize input data into a DataFrame.
    Args:
        data: List of manufacturing records
    Returns:
        Pandas DataFrame
    """
    return pd.DataFrame(data)

def transform_records(df: pd.DataFrame) -> pd.DataFrame:
    """Transform DataFrame to required schema.
    Args:
        df: Input DataFrame
    Returns:
        Transformed DataFrame
    """
    # Example transformation
    df['timestamp'] = pd.to_datetime(df['timestamp'])
    return df

def process_batch(df: pd.DataFrame) -> None:
    """Process a batch of manufacturing records.
    Args:
        df: DataFrame of records to process
    """
    # Connect to DuckDB and execute transformations
    connection = duckdb.connect(database=Config.database_url)
    connection.execute("CREATE TABLE IF NOT EXISTS manufacturing_data AS SELECT * FROM df")
    logger.info('Batch processed and stored in DuckDB.')
    connection.close()

def fetch_data() -> List[Dict[str, Any]]:
    """Fetch manufacturing data from an external source.
    Returns:
        List of manufacturing records
    """
    # Placeholder for actual data fetching logic
    return [{'id': 1, 'timestamp': '2023-10-01T12:00:00', 'value': 100}, {'id': 2, 'timestamp': '2023-10-01T12:05:00', 'value': 150}]

def save_to_db(df: pd.DataFrame) -> None:
    """Save transformed DataFrame to DuckDB.
    Args:
        df: DataFrame to save
    """
    process_batch(df)

def handle_errors(func):
    """Decorator for handling errors in data processing.
    Args:
        func: Function to decorate
    """
    def wrapper(*args, **kwargs):
        for attempt in range(Config.retry_attempts):
            try:
                return func(*args, **kwargs)
            except Exception as e:
                logger.error(f'Attempt {attempt + 1} failed: {e}')
                time.sleep(Config.retry_delay)  # Exponential backoff can be added here
        raise RuntimeError('All attempts failed')
    return wrapper

class AnalyticsPipeline:
    """Main orchestrator for the analytics pipeline.
    """
    @handle_errors
    def run(self, data: Dict[str, Any]) -> None:
        """Run the entire analytics pipeline.
        Args:
            data: Input data for processing
        """
        logger.info('Starting analytics pipeline...')
        validate_input(data)  # Validate input data
        sanitized_data = sanitize_fields(data['manufacturing_data'])  # Sanitize
        df = normalize_data(sanitized_data)  # Normalize
        transformed_df = transform_records(df)  # Transform
        save_to_db(transformed_df)  # Save
        logger.info('Analytics pipeline completed successfully.')

if __name__ == '__main__':
    # Example usage
    pipeline = AnalyticsPipeline()
    example_data = {'manufacturing_data': fetch_data()}
    pipeline.run(example_data)  # Run the pipeline