---

name: etl-designer description: Design ETL/ELT pipelines with proper orchestration, error handling, and monitoring. Use when building data pipelines, designing data workflows, or implementing data transformations.

ETL Designer

Design robust ETL/ELT pipelines for data processing.

Quick Start

Use Airflow for orchestration, implement idempotent operations, add error handling, monitor pipeline health.

Instructions

Airflow DAG Structure

Copyfrom airflow import DAG
from airflow.operators.python import PythonOperator
from datetime import datetime, timedelta

default_args = {
    'owner': 'data-team',
    'retries': 3,
    'retry_delay': timedelta(minutes=5),
    'email_on_failure': True,
    'email': ['alerts@company.com']
}

with DAG(
    'etl_pipeline',
    default_args=default_args,
    schedule_interval='0 2 * * *',  # Daily at 2 AM
    start_date=datetime(2024, 1, 1),
    catchup=False
) as dag:
    
    extract = PythonOperator(
        task_id='extract_data',
        python_callable=extract_from_source
    )
    
    transform = PythonOperator(
        task_id='transform_data',
        python_callable=transform_data
    )
    
    load = PythonOperator(
        task_id='load_to_warehouse',
        python_callable=load_to_warehouse
    )
    
    extract >> transform >> load

Incremental Processing

Copydef extract_incremental(last_run_date):
    query = f"""
        SELECT * FROM source_table
        WHERE updated_at > '{last_run_date}'
    """
    return pd.read_sql(query, conn)

Error Handling

Copydef safe_transform(data):
    try:
        transformed = transform_data(data)
        return transformed
    except Exception as e:
        logger.error(f"Transform failed: {e}")
        send_alert(f"Pipeline failed: {e}")
        raise

Best Practices

• Make operations idempotent
• Use incremental processing
• Implement proper error handling
• Add monitoring and alerts
• Use data quality checks
• Document pipeline logic