Validate and Profile Industrial Time Series Pipelines with dbt and Apache Iceberg

"""
Production implementation for validating and profiling industrial time series pipelines.
Utilizes dbt for transformation and Apache Iceberg for storage efficiency.
"""
from typing import Dict, Any, List, Tuple
import os
import logging
import time
import requests
from sqlalchemy import create_engine, text
from sqlalchemy.orm import sessionmaker

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

class Config:
    DATABASE_URL: str = os.getenv('DATABASE_URL')
    RETRY_LIMIT: int = 5
    RETRY_DELAY: float = 1.0  # seconds

engine = create_engine(Config.DATABASE_URL)
Session = sessionmaker(bind=engine)

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


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


def fetch_data(api_url: str) -> List[Dict[str, Any]]:
    """Fetch data from the specified API.
    
    Args:
        api_url: The API endpoint to fetch data from
    Returns:
        List[Dict[str, Any]]: List of records fetched from the API
    Raises:
        RuntimeError: If fetching fails
    """
    logger.info('Fetching data from API: %s', api_url)
    response = requests.get(api_url)
    if response.status_code != 200:
        raise RuntimeError(f"Failed to fetch data: {response.status_code}")
    return response.json()


def normalize_data(data: List[Dict[str, Any]]) -> List[Dict[str, Any]]:
    """Normalize data fields for consistency.
    
    Args:
        data: List of raw input data
    Returns:
        List[Dict[str, Any]]: Normalized data
    """
    normalized = []
    for record in data:
        normalized.append({
            'timestamp': record['timestamp'],
            'value': float(record['value'])
        })
    return normalized


def transform_records(data: List[Dict[str, Any]]) -> List[Tuple]:
    """Transform records into a format suitable for database insertion.
    
    Args:
        data: List of normalized records
    Returns:
        List[Tuple]: List of tuples for database insertion
    """
    return [(record['timestamp'], record['value']) for record in data]


def save_to_db(records: List[Tuple]):
    """Save transformed records to the database.
    
    Args:
        records: List of records to save
    Raises:
        Exception: If saving fails
    """
    logger.info('Saving records to the database')
    with Session() as session:
        for record in records:
            session.execute(text('INSERT INTO time_series (timestamp, value) VALUES (:timestamp, :value)'),
                             {'timestamp': record[0], 'value': record[1]})
        session.commit()


def process_batch(api_url: str) -> None:
    """Fetch, validate, transform and save data in a single batch.
    
    Args:
        api_url: API to fetch data from
    """
    logger.info('Processing batch from API')
    try:
        data = fetch_data(api_url)
        for entry in data:
            validate_input(entry)  # Validate each entry
            sanitized_entry = sanitize_fields(entry)
            normalized_data = normalize_data([sanitized_entry])  # Normalize
            transformed_records = transform_records(normalized_data)
            save_to_db(transformed_records)  # Save
    except Exception as e:
        logger.error('Error processing batch: %s', e)


def aggregate_metrics(session):
    """Aggregate metrics from the time series data.
    
    Args:
        session: Database session
    Returns:
        Dict[str, Any]: Aggregated metrics
    """
    result = session.execute(text('SELECT AVG(value) as avg_value FROM time_series')).fetchone()
    return {'average_value': result['avg_value']}


if __name__ == '__main__':
    api_url = os.getenv('API_URL')
    # Retry logic with exponential backoff
    for attempt in range(Config.RETRY_LIMIT):
        try:
            process_batch(api_url)
            break  # Exit loop if successful
        except Exception as e:
            logger.warning('Attempt %d failed: %s', attempt + 1, e)
            time.sleep(Config.RETRY_DELAY * (2 ** attempt))  # Exponential backoff
    else:
        logger.error('All attempts to process batch failed')