Prefect is a Python-based orchestration platform that enables data teams to create and manage resilient data pipelines with full observability and customizable infrastructure. It addresses the challenges of workflow complexity and failure recovery by allowing users to write pure Python code without boilerplate, while providing features like automatic retries and scheduling.
Funding
$46.1M raised to dateRaised to date based on public sources. This may differ from the amount the company actually raised and is based only on what is publicly available on the internet.
Founders
Product
Problem
Data teams face challenges in managing complex data pipelines, including workflow complexity, error handling, and infrastructure management. Traditional methods often involve boilerplate code and lack full observability, making it difficult to ensure pipeline resilience and recover from failures efficiently.
Solution
Prefect provides a Python-based orchestration platform designed to simplify the creation and management of data pipelines. It allows data engineers and ML engineers to write pure Python code without boilerplate, while offering features like automatic retries, scheduling, and prioritized alerting. The platform provides a control panel for orchestrating code and offers complete visibility into workflows, enabling users to quickly identify and address issues. Prefect allows users to develop locally and deploy to production, and it supports flexible infrastructure configuration.
Target Audience
Prefect is designed for data engineers and machine learning engineers who need to build, manage, and monitor complex data workflows with full observability and control.
Features
- Orchestration of Python code with scheduling, automatic retries, and prioritized instant alerting
- Full observability into workflows through a centralized control panel
- Pure Python coding environment without boilerplate requirements
- Custom retry behavior and caching mechanisms for rapid recovery from failures
- Local test server for easy local development and testing
- Granular control over infrastructure with work pools and work queues
- Integration with third-party tools for comprehensive visibility across the data stack