Features
Airflow, Dagster, and Prefect are open-source workflow orchestration frameworks that schedule, monitor, and manage data pipelines, each with distinct design philosophies and strengths.
Apache Airflow, Dagster, and Prefect sit at the heart of modern data engineering stacks. All three tools let you define, schedule, and observe complex data workflows—but they differ in API ergonomics, operational models, and ecosystem support. Understanding these differences is crucial for selecting the right orchestrator for your current and future needs.
Modern data systems comprise dozens of heterogeneous tasks: extract jobs, transformation queries, model training, reverse ETL, and more. Without an orchestrator, engineers end up with brittle cron scripts, hidden dependencies, and silent failures. A robust orchestrator:
Airflow, created at Airbnb in 2014 and now an Apache top-level project, popularized the concept of defining DAGs as Python code. Its mature scheduler and vast provider ecosystem make it the de-facto standard in many enterprises. Airflow favors declarative task definitions and stores metadata in a relational backend that powers its rich UI.
Dagster, released by Elementl, introduces a software-defined assets paradigm. Rather than centering on tasks, Dagster encourages developers to describe the data assets themselves—capturing lineage, metadata, and type information. Its strong typing and testing story make it feel more like a modern Python application framework.
Prefect started as a high-level API on top of Airflow’s scheduler but evolved into its own platform with an emphasis on hybrid execution: workflows run anywhere, but orchestration happens in Prefect Cloud or an open-source server. Its motto—"the negative engineering platform"—highlights its focus on failures, retries, and state handling out of the box.
@asset, @op) with built-in unit-testability. Asset-first design simplifies lineage.@flow, @task) feels natural for Python developers. Automatic parameter serialization.dagster-daemon for scheduling; can run workers via Kubernetes Jobs, ECS tasks, or Docker. Supports multi-repo deployments.dagster dev, Airflow’s airflow dags test, or Prefect’s prefect agent start --type=local for rapid feedback.Despite newer entrants, Airflow 2.x solved scheduler bottlenecks, and major companies continue to contribute. It remains vibrant.
Dagster’s asset model is optional. You can still build task-centric pipelines and gradually adopt assets.
Prefect provides an open-source orchestration server. You can self-host or migrate flows without code changes.
The following snippets implement a simple ELT that extracts orders from PostgreSQL, stages them in S3, loads into Snowflake, and refreshes a dbt model. Notice how each framework expresses similar logic with different abstractions.
from airflow import DAG
from airflow.providers.postgres.hooks.postgres import PostgresHook
from airflow.providers.amazon.aws.hooks.s3 import S3Hook
from airflow.operators.python import PythonOperator
from airflow.operators.dummy import DummyOperator
from airflow.utils.dates import days_ago
def extract_orders(**context):
pg = PostgresHook(postgres_conn_id="orders_db")
rows = pg.get_records("SELECT * FROM orders WHERE created_at >= now() - interval '1 day'")
# write to S3 ...
with DAG(
dag_id="nightly_elt",
schedule_interval="0 2 * * *",
start_date=days_ago(1),
catchup=False,
) as dag:
start = DummyOperator(task_id="start")
extract = PythonOperator(task_id="extract", python_callable=extract_orders)
load = BashOperator(
task_id="load_to_snowflake",
bash_command="python load.py",
)
dbt_run = BashOperator(task_id="dbt_run", bash_command="dbt run --select orders")
start >> extract >> load >> dbt_run
from dagster import job, op
@op
def extract_orders(context):
# extraction logic
return s3_key
@op
def load_to_snowflake(context, s3_key: str):
# load logic
@op
def dbt_run(context):
context.log.info("Running dbt ...")
@job
def nightly_elt():
dbt_run(load_to_snowflake(extract_orders()))
from prefect import flow, task
@task(retries=3)
def extract_orders():
# extraction logic
return s3_key
@task
def load_to_snowflake(s3_key):
# load logic
@task
def dbt_run():
print("Running dbt ...")
@flow(name="nightly-elt", log_prints=True)
def nightly_elt():
key = extract_orders()
load_to_snowflake(key)
dbt_run()
if __name__ == "__main__":
nightly_elt()
Although Galaxy focuses on interactive SQL editing—not orchestration—it complements these orchestrators. Engineers often prototype queries in Galaxy’s fast desktop editor, leverage its AI copilot to optimize SQL, and then embed the finalized statements into Airflow, Dagster, or Prefect tasks. By storing production-grade SQL in Galaxy Collections, teams can “endorse” validated queries and avoid pasting unvetted code into pipeline scripts, reducing errors downstream.
Choosing between Airflow, Dagster, and Prefect depends on your team’s priorities: maturity and ecosystem (Airflow), asset-centric design and type safety (Dagster), or rapid, Python-native development with strong failure semantics (Prefect). Evaluate existing skill sets, cloud architecture, and governance requirements before committing, and remember that adopting best practices—version control, testing, observability—matters more than the tool itself.
Selecting the right orchestrator determines how reliably data products are delivered, how quickly engineers debug failures, and how easily future requirements—like lineage, hybrid execution, or complex SLAs—can be met. A poor choice leads to brittle workflows, escalating ops costs, and slower insight delivery.
Yes. Airflow 2.x resolved many pain points, supports a vast ecosystem, and is backed by the Apache Foundation. It remains a safe, battle-tested choice.
While possible, it increases cognitive load. Instead, consolidate or segment by team responsibility. If you must, standardize logging and alerting.
Dagster’s dagster-airflow adapter converts Airflow DAGs to Dagster jobs. You then refactor to assets incrementally.
No, but Galaxy accelerates SQL authoring. You can develop, endorse, and version SQL in Galaxy, then call it from Airflow, Dagster, or Prefect tasks for more reliable pipelines.
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