Build Incremental Change-Tracked Factory Data Pipelines with LakeFS and Polars

"""
Production implementation for Building Incremental Change-Tracked Factory Data Pipelines with LakeFS and Polars.
Provides secure, scalable operations.
"""
from typing import Dict, Any, List, Optional
import os
import logging
import requests
import polars as pl
import time
import backoff

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

class Config:
    """
    Configuration class to manage environment variables.
    """
    lakefs_repo: str = os.getenv('LAKEFS_REPO')
    lakefs_branch: str = os.getenv('LAKEFS_BRANCH')
    database_url: str = os.getenv('DATABASE_URL')

@backoff.on_exception(backoff.expo, requests.exceptions.RequestException, max_tries=5)
def fetch_data(endpoint: str) -> List[Dict[str, Any]]:
    """
    Fetch data from a given endpoint.
    
    Args:
        endpoint: The API endpoint to fetch data from.
    Returns:
        List of dictionaries with fetched data.
    Raises:
        requests.exceptions.RequestException: If the request fails.
    """
    logger.info(f'Fetching data from {endpoint}')
    response = requests.get(endpoint)
    response.raise_for_status()
    return response.json()

def validate_input(data: Dict[str, Any]) -> bool:
    """
    Validate request data.
    
    Args:
        data: Input data to validate.
    Returns:
        True if valid, raises ValueError otherwise.
    Raises:
        ValueError: If validation fails.
    """
    if 'id' not in data:
        raise ValueError('Missing id')
    return True

def sanitize_fields(data: Dict[str, Any]) -> Dict[str, Any]:
    """
    Sanitize fields in the data.
    
    Args:
        data: Input data with fields to sanitize.
    Returns:
        Sanitized data.
    """
    return {k: v.strip() if isinstance(v, str) else v for k, v in data.items()}

def normalize_data(data: List[Dict[str, Any]]) -> pl.DataFrame:
    """
    Normalize input data into a Polars DataFrame.
    
    Args:
        data: List of dictionaries containing input data.
    Returns:
        Polars DataFrame with normalized data.
    """
    return pl.DataFrame(data)

def transform_records(df: pl.DataFrame) -> pl.DataFrame:
    """
    Transform records in the DataFrame for analysis.
    
    Args:
        df: Input DataFrame to transform.
    Returns:
        Transformed DataFrame.
    """
    # Example transformation: adding a new column
    return df.with_columns((pl.col('value') * 2).alias('transformed_value'))

def process_batch(df: pl.DataFrame) -> None:
    """
    Process a batch of records.
    
    Args:
        df: DataFrame with records to process.
    """
    # Process and save to LakeFS
    logger.info('Processing batch of records')
    # Here you would implement the logic to save to LakeFS

def aggregate_metrics(df: pl.DataFrame) -> Dict[str, Any]:
    """
    Aggregate metrics from the DataFrame.
    
    Args:
        df: Input DataFrame to aggregate metrics from.
    Returns:
        Dictionary with aggregated metrics.
    """
    return {
        'count': df.height,
        'sum': df['value'].sum(),
    }

def save_to_db(data: Dict[str, Any]) -> None:
    """
    Save processed data to the database.
    
    Args:
        data: Processed data to save.
    """
    logger.info('Saving processed data to the database')
    # Implement database saving logic here

class DataPipeline:
    """
    Main orchestrator class for the incremental data pipeline.
    """
    def __init__(self, config: Config):
        self.config = config

    def run(self, endpoint: str) -> None:
        """
        Run the complete data pipeline workflow.
        
        Args:
            endpoint: The API endpoint to fetch data from.
        """
        try:
            # Fetch data
            raw_data = fetch_data(endpoint)
            # Validate input data
            for item in raw_data:
                validate_input(item)
                sanitized_item = sanitize_fields(item)
                # Normalize data
                df = normalize_data([sanitized_item])
                # Transform records
                transformed_df = transform_records(df)
                # Process batch
                process_batch(transformed_df)
                # Aggregate metrics
                metrics = aggregate_metrics(transformed_df)
                logger.info(f'Aggregated metrics: {metrics}')
                # Save to database
                save_to_db(metrics)
        except Exception as e:
            logger.error(f'Error occurred: {e}')
        finally:
            logger.info('Pipeline execution completed, cleaning up resources')

if __name__ == '__main__':
    # Example usage
    config = Config()
    pipeline = DataPipeline(config)
    pipeline.run('https://api.example.com/data')