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Tool Comparison
VS
At a Glance
| Attribute | Apache Airflow | Prefect |
|---|---|---|
| License / Pricing | Open Source | Open Source |
| Type | data | data |
| GitHub Stars | — | — |
| Rating | 4.5/5 | 4.5/5 |
| Key Features | 6 listed | 6 listed |
| Integrations | 5 listed | 5 listed |
| Categories | Workflow Orchestration | Workflow Orchestration |
Key Features
Apache Airflow
- DAGs defined as Python code with rich scheduling expressions (cron, timetables)
- Rich UI for monitoring task execution, logs, and Gantt charts
- 250+ provider packages for AWS, GCP, Azure, Spark, dbt, and more
- Dynamic task mapping for parallel fan-out on variable-size inputs
- Managed Airflow via Amazon MWAA, Google Cloud Composer, and Astronomer
- XCom for passing data between tasks with flexible backend storage
Prefect
- Decorate Python functions with @flow and @task — no DAG rewrite
- Automatic retries, timeouts, and caching with minimal config
- Prefect Cloud for managed scheduling, logging, and alerting
- Work pools and workers for flexible infrastructure deployment
- Deployments for versioned, parameterized workflow runs
- Native async support and dynamic task generation
Real-World Use Cases
Apache Airflow
Orchestrate a dbt + Spark ETL pipeline
Define a DAG with SparkSubmitOperator to run a transformation job
ML training pipeline orchestration
Create tasks for data ingestion, feature engineering, model training
Prefect
Convert an existing Python ETL script to a workflow
Add @flow to the main function and @task to individual steps
Parameterized data pipeline with dynamic tasks
Define flow parameters (date_range, source_table) using Pydantic models
Integrations
Apache Airflow
apache-sparkdbtairbytesnowflakegreat-expectations
Prefect
dbtairbyteapache-sparksnowflakedagster
🏆 Which should you choose?
Choose Apache Airflow if…
- → you're already in the Workflow Orchestration ecosystem and prefer Apache Airflow's workflow
Choose Prefect if…
- → you're already in the Workflow Orchestration ecosystem and prefer Prefect's workflow