Asset Management

class Asset:
    """
    Version-compatible asset/dataset class.
    
    Maps to airflow.assets.Asset in Airflow 3.0+
    Maps to airflow.datasets.Dataset in Airflow < 3.0
    """

class AssetAlias:
    """
    Version-compatible asset/dataset alias class.
    
    Maps to airflow.assets.AssetAlias in Airflow 3.0+
    Maps to airflow.datasets.DatasetAlias in Airflow < 3.0
    """

class AssetAll:
    """
    Asset collection representing all assets (AND logic).
    
    Maps to airflow.assets.AssetAll in Airflow 3.0+
    Maps to airflow.datasets.DatasetAll in Airflow < 3.0
    """

class AssetAny:
    """
    Asset collection representing any asset (OR logic).
    
    Maps to airflow.assets.AssetAny in Airflow 3.0+
    Maps to airflow.datasets.DatasetAny in Airflow < 3.0
    """

class AssetDetails:
    """
    Asset details for authorization purposes.
    
    Maps to airflow.assets.AssetDetails in Airflow 3.0+
    Maps to airflow.datasets.DatasetDetails in Airflow < 3.0
    """

class AssetAliasDetails:
    """
    Asset alias details for authorization purposes.
    
    Available in Airflow 3.0+ from airflow.api_fastapi.auth.managers.models.resource_details.AssetAliasDetails
    Note: May not be properly imported in Airflow < 3.0 despite being in __all__
    """

def expand_alias_to_assets(...):
    """
    Expand asset aliases to actual assets.
    
    Maps to appropriate expansion function based on Airflow version.
    """

from airflow.providers.common.compat.assets import (
    Asset,
    AssetAlias,
    AssetAll,
    AssetAny,
    expand_alias_to_assets
)

# Create assets
input_data = Asset("s3://bucket/input.csv")
output_data = Asset("s3://bucket/output.csv")

# Create asset aliases
processed_data = AssetAlias("processed_data")

# Use asset collections
all_inputs = AssetAll([input_data, processed_data])
any_output = AssetAny([output_data])

# Expand aliases
actual_assets = expand_alias_to_assets(processed_data)

# Use in DAGs (asset-aware scheduling)
from airflow import DAG
from airflow.operators.dummy import DummyOperator

dag = DAG(
    "example_dag",
    schedule=[input_data],  # Trigger when input_data is updated
    catchup=False
)

task = DummyOperator(
    task_id="process_data",
    outlets=[output_data],  # Mark as producing output_data
    dag=dag
)