# Orchestration

> Skills for generating and deploying Airflow and Dagster DAGs

2 skills for generating and deploying pipeline orchestration with Apache Airflow and Dagster.

## Skills

### dag-generate

**Generate orchestration DAGs from pipeline configuration.** Automatically creates Airflow or Dagster definitions based on your domain and task dependencies.

```
You: /dag-generate Create Airflow DAGs for all domains with daily schedule and email alerts
```

**Supported orchestrators:**
- **Apache Airflow**: Python DAG files with operators
- **Dagster**: Asset-based pipeline definitions

**Generated outputs:**
- DAG definition files with correct task dependencies
- Schedule configurations (cron expressions)
- Alerting and retry policies
- Connection references
- Environment-specific parameterization

### Airflow Example

```python
# Generated by Starlake dag-generate
from airflow import DAG
from airflow.operators.bash import BashOperator
from datetime import datetime, timedelta

default_args = {
    'owner': 'starlake',
    'depends_on_past': False,
    'email_on_failure': True,
    'retries': 2,
    'retry_delay': timedelta(minutes=5),
}

with DAG(
    'starlake_customers_load',
    default_args=default_args,
    schedule_interval='0 6 * * *',
    start_date=datetime(2024, 1, 1),
    catchup=False,
) as dag:
    stage = BashOperator(
        task_id='stage_customers',
        bash_command='starlake stage --domain customers',
    )
    load = BashOperator(
        task_id='load_customers',
        bash_command='starlake load --domain customers',
    )
    stage >> load
```

---

### dag-deploy

**Deploy generated DAGs** to your orchestration platform.

```
You: /dag-deploy Deploy the generated Airflow DAGs to our Cloud Composer environment
```

**Deployment targets:**
- Local Airflow (file copy to dags folder)
- Cloud Composer (GCS upload)
- MWAA (S3 upload)
- Dagster Cloud
- Docker-based deployments