Workflow Orchestration Comparison
5 min read Quiz at the end
Airflow for complex DAGs, Prefect for Pythonic flows, Dagster for asset-centric data — choose by team style.
Orchestration Tools Comparison
| Tool | Best For | Key Concept | Hosting |
|---|
| Airflow | Complex DAGs, legacy | DAG + Operators | Self-hosted or MWAA |
| Prefect | Modern Python, rapid dev | Flow + Tasks | Prefect Cloud or self |
| Dagster | Asset-centric, software-defined | Asset + Op | Dagster Cloud or self |
| dbt Cloud | SQL transformations only | Model + Test | Managed SaaS |
| Temporal | Long-running workflows, durability | Workflow + Activity | Self-hosted or Cloud |
| Argo Workflows | K8s-native, container per step | DAG in YAML | K8s cluster |
# Dagster: software-defined assets
from dagster import asset, define_asset_job
@asset
def raw_orders() -> pd.DataFrame:
return extract_from_crm()
@asset
def cleaned_orders(raw_orders: pd.DataFrame) -> pd.DataFrame:
return raw_orders.dropna().rename(columns={...})
@asset
def monthly_revenue(cleaned_orders: pd.DataFrame) -> pd.DataFrame:
return cleaned_orders.groupby('month')['amount'].sum().reset_index()
# Dagster knows the asset lineage automatically
# Re-materialise any asset and its dependents
daily_job = define_asset_job('daily_refresh',
selection=[raw_orders, cleaned_orders, monthly_revenue])
dagster dev # run UI at localhost:3000