Prefect
Python-native workflow orchestration platform for building, scheduling, and monitoring data pipelines with ordinary Python code.
- Actively maintained
- Well documented
- Permissive license
Overview
Prefect lets developers define workflows as plain Python functions decorated as tasks and flows, rather than learning a separate DSL or YAML format. It handles scheduling, retries, caching, and failure handling automatically, and its UI gives visibility into every run without extra instrumentation code.
Prefect is a fit for data and platform teams building ETL, ML, or data-pipeline workflows in Python who want orchestration that stays close to regular code - a different niche than Temporal or Argo Workflows (also in this catalog), which are aimed at general-purpose distributed application workflows rather than data-pipeline-specific scheduling.
Why it's in this catalog
Catalog rating: ★★★☆☆ (3/5)
Popular Python-native workflow orchestration, meaningful overlap with Kestra and Airflow-style tools.
- Mature
- Vendor-backed
- Categories
- Automation
- Languages
- Python
- License
- Apache-2.0
Related Projects
Kestra
Apache-2.0Declarative, YAML-based orchestration platform for scheduling and orchestrating data pipelines, ETL jobs, and business workflows.
Actively maintained
- Automation
Argo Workflows
Apache-2.0Kubernetes-native workflow engine that runs each step of a pipeline as a container, orchestrating parallel jobs and multi-step DAGs directly on a cluster.
Actively maintained
- Automation
Temporal
MITDurable execution platform that lets developers write reliable, long-running workflows as ordinary code, with retries, state, and failure recovery handled automatically.
Actively maintained
- Automation
SeaTunnel
Apache-2.0Distributed data integration platform for moving and transforming data between databases, data lakes, and messaging systems at scale.
Actively maintained
- Backend
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