Prefect
ActiveDescription
A workflow orchestration framework for building resilient data pipelines and AI workflows in Python, with task scheduling, state management, and failure recovery from local to distributed deployments.
Key Features
- Flow and task decorators — declare Python functions as orchestrable workflows with @flow and @task
- Auto-retry and error handling — built-in task-level retries, caching, parametric execution
- Scheduled deployments — cron expression and event-triggered automated scheduling
- Visual monitoring dashboard — Prefect Server UI shows real-time workflow status, logs, dependencies
- Distributed execution — elastic deployment from local to Kubernetes/Docker, parallel task support
- Event-driven automation — trigger downstream actions based on workflow events for reactive pipelines
Use Cases
💡 Building resilient ETL data pipelines (extract, transform, load)
💡 Orchestrating AI/ML training and inference workflows
💡 Automating scheduled data reports and monitoring tasks
💡 Managing multi-step data science experiment pipelines
💡 Building scalable data workflows from local dev to production
Strengths & Limitations
✅ Strengths
- • Actively maintained, recent updates
- • High community interest (23.8k stars)
- • Permissive open-source license (Apache-2.0)
Categories
Quick Start
pip install -U prefect
from prefect import flow, task
import httpx
@task(log_prints=True)
def get_stars(repo: str):
url = f"https://api.github.com/repos/{repo}"
count = httpx.get(url).json()["stargazers_count"]
print(f"{repo} has {count} stars!")
@flow(name="GitHub Stars")
def github_stars(repos: list[str]):
for repo in repos:
get_stars(repo)
if __name__ == "__main__":
github_stars(["PrefectHQ/prefect"])