Airflow vs. Prefect vs. Dagster: A Data Engineer's Guide

Choosing the right workflow orchestration tool is crucial for your data engineering success. Let's compare the three most popular options.

Overview

Apache Airflow

• Maturity: Most mature (2014)
• Community: Largest (20,000+ GitHub stars)
• Language: Python-focused, but supports multiple
• Architecture: DAG-based, scheduler-driven

Prefect

• Maturity: Modern (2018)
• Community: Growing rapidly (15,000+ stars)
• Language: Python-first
• Architecture: Flow-based, agent-driven

Dagster

• Maturity: Newer (2018)
• Community: Growing (8,000+ stars)
• Language: Python-first
• Architecture: Data-aware, asset-based

Feature Comparison

Use Case Recommendations

Choose Airflow If:

• You need the most mature, battle-tested solution
• You have a large team with Airflow experience
• You need extensive integrations (100+ providers)
• You're building complex, long-running workflows
• You need enterprise-grade features

Choose Prefect If:

• You want a modern Python-first approach
• You need better testing and development experience
• You prefer simpler local development
• You want built-in observability
• You're building new projects

Choose Dagster If:

• You need data-aware orchestration
• You want asset-based workflows
• You need strong data lineage tracking
• You're building data platforms
• You want modern developer experience

Code Comparison

Airflow Example

from 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),
}

dag = DAG(
    'etl_pipeline',
    default_args=default_args,
    schedule_interval='@daily',
    start_date=datetime(2024, 1, 1),
)

extract_task = PythonOperator(
    task_id='extract',
    python_callable=extract_data,
    dag=dag,
)

transform_task = PythonOperator(
    task_id='transform',
    python_callable=transform_data,
    dag=dag,
)

extract_task >> transform_task

Prefect Example

from prefect import flow, task

@task
def extract_data():
    return "data"

@task
def transform_data(data):
    return f"transformed_{data}"

@flow
def etl_pipeline():
    data = extract_data()
    result = transform_data(data)
    return result

if __name__ == "__main__":
    etl_pipeline()

Dagster Example

from dagster import asset, job, op

@asset
def raw_data():
    return "data"

@asset
def transformed_data(raw_data):
    return f"transformed_{raw_data}"

@job
def etl_job():
    transformed_data()

Migration Considerations

From Airflow to Prefect/Dagster:

• Effort: Medium to High
• Benefits: Better DX, modern features
• Risks: Learning curve, ecosystem differences

Staying with Airflow:

• Effort: Low (if already using)
• Benefits: Mature ecosystem, large community
• Risks: Steeper learning curve for new team members

Conclusion

For Most Teams:

• Stick with Airflow if you're already using it and it works
• Consider Prefect for new Python-focused projects
• Consider Dagster for data-aware workflows

All three tools are excellent choices. The best tool depends on your specific needs, team expertise, and project requirements.

If you want a printable matrix and more detailed analysis (including Temporal), download our Data Engineering Tools Comparison Guide.

Pro Tip: Tools like DAGForge work with Airflow, helping you build DAGs faster regardless of which orchestration tool you choose.