Enrich Sensor Telemetry for Predictive Maintenance with Bytewax and PyIceberg

"""
Production implementation for enriching sensor telemetry for predictive maintenance.
This module integrates Bytewax for stream processing and PyIceberg for data management.
"""
import os
import logging
from typing import Dict, Any, List, Tuple
from bytewax.dataflow import Dataflow
from bytewax import run
from pyiceberg import Table
from pyiceberg.exceptions import TableNotFoundError

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

class Config:
    """
    Configuration class to handle environment variables.
    """
    database_url: str = os.getenv('DATABASE_URL')
    iceberg_table: str = os.getenv('ICEBERG_TABLE')

def validate_input(data: Dict[str, Any]) -> bool:
    """Validate incoming telemetry data.
    
    Args:
        data: Input data to validate
    Returns:
        True if data is valid
    Raises:
        ValueError: If validation fails
    """  
    if 'sensor_id' not in data or 'value' not in data:
        raise ValueError('Missing sensor_id or value in data')
    if not isinstance(data['value'], (int, float)):
        raise ValueError('Value must be a number')
    return True

def sanitize_fields(data: Dict[str, Any]) -> Dict[str, Any]:
    """Sanitize input data fields.
    
    Args:
        data: Incoming 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 telemetry data for consistency.
    
    Args:
        data: Input data to normalize
    Returns:
        Normalized data
    """  
    data['value'] = round(data['value'], 2)  # Round to 2 decimal places
    return data

def fetch_data() -> List[Dict[str, Any]]:
    """Fetch telemetry data from the source.
    
    Returns:
        List of telemetry data entries
    Raises:
        RuntimeError: If data fetching fails
    """  
    # Simulate fetching data from an external source
    logger.info('Fetching data from external source...')
    # Here we would normally connect to a data source
    return [{"sensor_id": "sensor_1", "value": 23.5}, {"sensor_id": "sensor_2", "value": 19.0}]

def transform_records(records: List[Dict[str, Any]]) -> List[Dict[str, Any]]:
    """Transform records for processing.
    
    Args:
        records: List of raw telemetry records
    Returns:
        List of transformed records
    """  
    return [normalize_data(sanitize_fields(record)) for record in records]

def aggregate_metrics(records: List[Dict[str, Any]]) -> Dict[str, float]:
    """Aggregate metrics from records for analysis.
    
    Args:
        records: List of telemetry records
    Returns:
        Dictionary of aggregated metrics
    """  
    total_value = sum(record['value'] for record in records)
    average_value = total_value / len(records) if records else 0
    return {'total_value': total_value, 'average_value': average_value}

def save_to_db(data: List[Dict[str, Any]]) -> None:
    """Save processed data to the database.
    
    Args:
        data: List of data to save
    Raises:
        Exception: If saving data fails
    """  
    logger.info('Saving data to the database...')
    # Here we would use a connection pool to save data efficiently
    try:
        # Simulate saving to the database
        for record in data:
            logger.info(f'Saving record: {record}')
            # Assume a function to save individual records
            pass
    except Exception as e:
        logger.error(f'Error saving data: {e}')
        raise

class TelemetryEnrichment:
    """Main orchestrator for enriching telemetry data.
    """
    def __init__(self, config: Config) -> None:
        self.config = config

    def process(self) -> None:
        """Main processing workflow.
        """  
        try:
            raw_data = fetch_data()  # Step 1: Fetch data
            validated_data = [record for record in raw_data if validate_input(record)]  # Step 2: Validate
            transformed_data = transform_records(validated_data)  # Step 3: Transform
            aggregated_metrics = aggregate_metrics(transformed_data)  # Step 4: Aggregate metrics
            save_to_db(transformed_data)  # Step 5: Save to DB
            logger.info(f'Aggregated metrics: {aggregated_metrics}')  # Log metrics
        except Exception as e:
            logger.error(f'Error during processing: {e}')

def main() -> None:
    """Main function to run the enrichment process.
    """  
    config = Config()  # Load config
    telemetry_enrichment = TelemetryEnrichment(config)  # Initialize the enrichment process
    telemetry_enrichment.process()  # Execute the process

if __name__ == '__main__':
    main()  # Execute the script