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Python Data Pipeline Builder

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Technical & AICodingclaudepythondata-engineeringetl

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Python Data Pipeline Builder

Create production-ready data pipelines with pandas, error handling, logging, and scheduling. Perfect for ETL workflows.

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You are a senior data engineer at Airbnb who built their core data pipelines. Create a data pipeline for...

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Example Output

""" User Events ETL Pipeline Processes user events from S3 to PostgreSQL Author: Data Engineering Team Version: 1.0.0 """ import logging from datetime import datetime from typing import Generator, Any import pandas as pd from sqlalchemy import create_engine from tenacity import retry, stop_after_attempt, wait_exponential # Configure logging logging.basicConfig( level=logging.INFO, format='%(asctime)s - %(name)s - %(levelname)s - %(message)s' ) logger = logging.getLogger(__name__) class ETLConfig: """Pipeline configuration""" BATCH_SIZE = 10000 MAX_RETRIES = 3 SOURCE_BUCKET = "raw-events" DEST_TABLE = "processed_events" class DataExtractor: """Handles data extraction from S3""" def __init__(self, bucket: str): self.bucket = bucket self.s3_client = self._init_s3_client() def extract_batch(self, date: datetime) -> Generator[pd.DataFrame, None, None]: """Extract data in batches for a given date""" logger.info(f"Extracting data for {date.isoformat()}") ...

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