Turn Python scripts into resilient data pipelines
Imagine daily data jobs stopping due to unexpected errors, forcing you to rerun them manually. Prefect turns Python scripts into automated workflows with scheduling, retries, and caching by modifying just a few lines of code. It is a workflow orchestration framework that lets data teams operate stable data processing without building complex infrastructure themselves.
Structure for converting scripts to workflows
Prefect uses Python's flow and task decorators to convert existing scripts into workflows. This structure handles dependencies and branching logic between tasks and includes built-in automatic retries on failure. Users can deploy workflows using the serve method and set execution schedules with cron expressions. You can also configure workflows to react to events. These features help data pipelines adapt to changes in the external environment and recover from unexpected modifications.
Execution tracking and environment selection
Workflow activity can be tracked on a self-hosted Prefect server or the managed Prefect Cloud dashboard. Running the prefect server start command locally opens the UI on port 4200 in your browser to check execution results. For lightweight environments that only need to communicate with a remote server or Cloud, you can use prefect-client.
Conditions to check before adoption
Using Prefect requires Python version 3.11 or higher. To integrate with agents, refer to the guides for connecting via plugins or MCP. The community includes over 25,000 practitioners who provide support through documentation, the Slack community, and the developer blog. Before adopting it, it is recommended to verify that your data processing scale and monitoring requirements align with the feature scope of Prefect Cloud and that you meet the Python version requirements.
