Version Sensor Data with DVC and Vertex AI SDK

                      
                     
"""
Production implementation for Version Sensor Data with DVC and Vertex AI SDK.
Provides secure, scalable operations for sensor data management.
"""

from typing import Dict, Any, List
import os
import logging
import time
import dvc.api
from google.cloud import aiplatform

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

class Config:
    dvc_repo: str = os.getenv('DVC_REPO', 'my_dvc_repo')
    model_name: str = os.getenv('MODEL_NAME', 'sensor-model')
    project_id: str = os.getenv('PROJECT_ID', 'my-project')
    location: str = os.getenv('LOCATION', 'us-central1')

async def validate_input(data: Dict[str, Any]) -> bool:
    """Validate sensor data input.
    Args:
        data: Input data to validate
    Returns:
        True if valid
    Raises:
        ValueError: If validation fails
    """
    required_fields = ['sensor_id', 'timestamp', 'value']
    for field in required_fields:
        if field not in data:
            raise ValueError(f'Missing required field: {field}')
    return True

async def sanitize_fields(data: Dict[str, Any]) -> Dict[str, Any]:
    """Sanitize input fields.
    Args:
        data: Raw input data
    Returns:
        Sanitized data
    """
    data['sensor_id'] = str(data['sensor_id']).strip()
    data['value'] = float(data['value'])  # Ensure value is a float
    return data

async def fetch_data(sensor_id: str) -> Dict[str, Any]:
    """Fetch sensor data from DVC.
    Args:
        sensor_id: ID of the sensor
    Returns:
        Data fetched from DVC
    """
    with dvc.api.open(f'data/{sensor_id}.json', repo=Config.dvc_repo) as fd:
        return fd.read()

async def transform_records(records: List[Dict[str, Any]]) -> List[Dict[str, Any]]:
    """Transform raw records for processing.
    Args:
        records: List of raw sensor records
    Returns:
        Transformed records
    """
    transformed = []
    for record in records:
        transformed.append({
            'sensor_id': record['sensor_id'],
            'timestamp': record['timestamp'],
            'value': record['value'] * 1.1  # Example transformation
        })
    return transformed

async def save_to_db(data: List[Dict[str, Any]]) -> None:
    """Save data to the database.
    Args:
        data: List of data to save
    """
    # Simulating a database save
    logger.info('Saving data to database...')
    time.sleep(1)  # Simulate delay
    logger.info('Data saved successfully.')

async def call_api(data: Dict[str, Any]) -> None:
    """Call external API with processed data.
    Args:
        data: Data to send to the API
    """
    logger.info('Calling external API...')
    time.sleep(1)  # Simulate API call
    logger.info('API call successful.')

async def aggregate_metrics(data: List[Dict[str, Any]]) -> Dict[str, Any]:
    """Aggregate metrics from data.
    Args:
        data: List of sensor data
    Returns:
        Aggregated metrics
    """
    total_value = sum(record['value'] for record in data)
    return {'total_value': total_value}

class SensorDataProcessor:
    """Main orchestrator for processing sensor data.
    """
    async def process(self, data: Dict[str, Any]) -> None:
        try:
            # Validate the input data
            await validate_input(data)
            # Sanitize fields
            sanitized_data = await sanitize_fields(data)
            # Fetch existing data from DVC
            existing_data = await fetch_data(sanitized_data['sensor_id'])
            # Combine existing and new data
            combined_data = existing_data + [sanitized_data]
            # Transform records for processing
            transformed_data = await transform_records(combined_data)
            # Aggregate metrics
            metrics = await aggregate_metrics(transformed_data)
            logger.info('Aggregated Metrics: %s', metrics)
            # Save to database
            await save_to_db(transformed_data)
            # Call external API
            await call_api(metrics)
        except ValueError as ve:
            logger.error(f'Value error: {ve}')
        except Exception as e:
            logger.error(f'An error occurred: {e}')

if __name__ == '__main__':
    # Example usage
    processor = SensorDataProcessor()
    example_data = {
        'sensor_id': 'sensor_1',
        'timestamp': '2023-10-01T12:00:00Z',
        'value': 25.5
    }
    import asyncio
    asyncio.run(processor.process(example_data))