Apache Airflow is a platform to programmatically author, schedule, and monitor workflows. When workflows are defined as code, they become more maintainable, versionable, testable, and collaborative. Find open source and proprietary alternatives that serve similar purposes.
Open source projects that can replace Apache Airflow:
Prefect is a powerful workflow orchestration framework that helps data teams transform Python scripts into production-ready data pipelines. It provides the tools and visibility needed to build resilient, automated workflows that can handle complex dependencies, retries, and monitoring requirements.
Simple Python-Native Workflows: Prefect uses decorators to transform regular Python functions into observable workflows. The @flow
and @task
decorators make it easy to define and orchestrate complex pipelines while maintaining pure Python syntax.
Robust Error Handling: Built-in support for retries, timeouts, and failure notifications helps ensure workflow reliability. Workflows can automatically recover from transient failures and notify teams when intervention is needed.
Flexible Scheduling & Triggers: Workflows can be scheduled using cron expressions or triggered by events. The platform supports complex scheduling patterns and event-driven execution.
Comprehensive Monitoring: The Prefect UI provides real-time visibility into workflow execution, logs, and metrics. Teams can track workflow health and troubleshoot issues through a modern dashboard interface.
Cloud or Self-Hosted: Choose between Prefect Cloud for a managed experience or self-host the Prefect server for complete control. Both options provide the same core orchestration capabilities.
Prefect is ideal for:
Prefect can be installed via pip and requires Python 3.9+. The platform provides a local development server for testing and a production server for deployment. Basic workflows can be created with just a few lines of code:
Whether you're building simple data pipelines or complex ML workflows, Prefect provides the orchestration capabilities needed for modern data stack automation while maintaining the simplicity of pure Python.
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