Reproduce Industrial Data Lake Snapshots for Compliance Audits with LakeFS and DuckDB

"""
Production implementation for reproducing industrial data lake snapshots for compliance audits.
Provides secure, scalable operations using LakeFS and DuckDB.
"""

from typing import Dict, Any, List
import os
import logging
import time
import duckdb
from lakefs_client import LakefsClient

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

class Config:
    """
    Configuration class to hold environment variables.
    """
    lakefs_url: str = os.getenv('LAKEFS_URL')
    lakefs_access_key: str = os.getenv('LAKEFS_ACCESS_KEY')
    lakefs_secret_key: str = os.getenv('LAKEFS_SECRET_KEY')
    duckdb_path: str = os.getenv('DUCKDB_PATH', 'data.db')

# Initialize LakeFS client
lakefs_client = LakefsClient(
    config=Config()
)

async def validate_input(data: Dict[str, Any]) -> None:
    """Validate request data.
    
    Args:
        data: Input to validate
    Raises:
        ValueError: If validation fails
    """
    # Check for required fields
    if 'snapshot_id' not in data:
        raise ValueError('Missing snapshot_id')
    if 'repository' not in data:
        raise ValueError('Missing repository')

async def fetch_data(snapshot_id: str, repository: str) -> List[Dict[str, Any]]:
    """Fetch snapshot data from LakeFS.
    
    Args:
        snapshot_id: ID of the snapshot
        repository: Name of the repository
    Returns:
        List of records from the snapshot
    """
    # Fetching snapshot data
    logger.info(f'Fetching data for snapshot: {snapshot_id} from repository: {repository}')
    try:
        records = lakefs_client.fetch_snapshot_data(repository, snapshot_id)
    except Exception as e:
        logger.error(f'Error fetching data: {e}')
        raise
    return records

async def transform_records(records: List[Dict[str, Any]]) -> List[Dict[str, Any]]:
    """Transform raw records.
    
    Args:
        records: List of raw records
    Returns:
        List of transformed records
    """
    # Normalize data fields
    transformed = []
    for record in records:
        transformed_record = {key: str(value) for key, value in record.items()}
        transformed.append(transformed_record)
    logger.info('Records transformed successfully')
    return transformed

async def save_to_db(records: List[Dict[str, Any]]) -> None:
    """Save transformed records to DuckDB.
    
    Args:
        records: List of records to save
    """
    # Connecting to DuckDB
    conn = duckdb.connect(database=Config.duckdb_path)
    try:
        # Creating table if not exists
        conn.execute('CREATE TABLE IF NOT EXISTS snapshots (snapshot_id VARCHAR, data JSON)')
        for record in records:
            conn.execute('INSERT INTO snapshots VALUES (?, ?)', (record['snapshot_id'], record))
        logger.info('Records saved to DuckDB successfully')
    except Exception as e:
        logger.error(f'Error saving to DB: {e}')
        raise
    finally:
        conn.close()  # Ensure connection is closed

async def process_batch(data: Dict[str, Any]) -> None:
    """Process the data batch for snapshots.
    
    Args:
        data: Batch of data containing snapshot_id and repository
    """
    await validate_input(data)  # Validate input data
    snapshot_id = data['snapshot_id']
    repository = data['repository']
    records = await fetch_data(snapshot_id, repository)  # Fetch the data
    transformed = await transform_records(records)  # Transform the records
    await save_to_db(transformed)  # Save to DuckDB

if __name__ == '__main__':
    # Example usage
    sample_data = {
        'snapshot_id': '12345',
        'repository': 'my_repository'
    }
    try:
        # Run the processing as a coroutine
        import asyncio
        asyncio.run(process_batch(sample_data))
    except Exception as e:
        logger.error(f'Error processing batch: {e}')