Prefect
Prefect is an open-source Python workflow orchestration framework for building resilient data pipelines with full observability and zero rewrites.
Prefect is a modern, flexible workflow orchestration framework that turns any Python function into a production-ready pipeline. It adds workflow execution and observability to Python pipelines.
Key Features:
- Python-Native - Add one decorator to transform any function into an observable workflow
- Zero Lock-In - Self-hosted and Apache 2.0 licensed with full source access
- Managed Cloud - Enterprise SSO, autoscaling workers, and SOC 2 Type II compliance
- FastMCP Integration - Build MCP servers to connect AI agents to any data system
- Real-Time Observability - Debug pipelines with built-in tracing, logging, and alerting
Whether you're a data engineer, ML team, or scraping pipeline builder, Prefect provides resilient orchestration from script to scale.
Web scraping workflow
How to use Prefect in a web scraping pipeline
Prefect turns Python functions into tracked flows with parameters, retries and timeout controls. It can wrap an existing scraping script so collection becomes an observable run with explicit inputs and a recorded outcome.
A practical workflow
- Start a flow with a URL manifest, crawl date and destination prefix as parameters.
- Call your HTTP or browser extraction code, then run validation and loading functions against its saved results.
- Return a dataset reference and inspect the flow state when a run fails or stops before publication.
When it fits
Use it when an existing Python collection routine needs operational structure without rewriting its parsing logic. Parameterized runs help reproduce a failure against the same inputs and distinguish one refresh from another.
Boundaries
The execution environment still needs your scraper dependencies, network access and storage credentials. A flow retry can repeat writes or billable service requests, so use idempotent publication and bounded attempts. Keep full HTML and screenshots in external storage rather than treating the orchestration database as a capture archive.
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Our productRepository
PrefectHQ/prefect
Stars
24KForks
2.6KLast commit
2026-10-15License
Apache-2.0Language
Python
Metrics recorded 2026-10-16
Repository details
License source: Repository license file
Frequently asked questions
Is Prefect an ETL tool?
Prefect orchestrates workflows written in Python. Those workflows can perform extraction, transformation and loading, but your tasks implement or invoke the data-processing operations. Prefect coordinates execution, state and retries.
Sources (checked ): Prefect introduction; Prefect flows
What are some alternatives to Prefect?
Airflow and Dagster also orchestrate data workflows. Airflow organizes tasks as Python DAGs; Dagster emphasizes data assets and their dependencies. Compare authoring, scheduling, observability and deployment needs rather than treating these models as identical.
Sources (checked ): Prefect introduction; Airflow documentation; Dagster documentation
What are the key differences between Airflow and Prefect?
Prefect adds orchestration to Python functions using flows and tasks, including runtime control flow. Airflow defines DAGs of tasks and manages their scheduling and execution. Compare how each represents dependencies and how you will deploy and observe the workflow.
Sources (checked ): Prefect flows; Airflow documentation
Sources
Verified against the PrefectHQ/prefect repository and the PyPI package.