Open-source Astronomer alternatives

Apache Airflow is a workflow platform where you write, schedule and watch over pipelines in code. Workflows are defined as Python code in the form of directed acyclic graphs, or DAGs, where each task and its dependencies are explicit, so pipelines can be versioned, tested and reviewed like any other software.

A scheduler runs tasks on a defined cadence and workers execute them, while a web interface shows the status of each run, lets you inspect logs and retry failures. Airflow is used heavily in data engineering, including data integration, ELT and ETL pipelines, data orchestration and machine learning workflows, and its topics cover data science and automation. It has a large ecosystem of providers and operators for connecting to databases, cloud services and other systems, and the 3.x line is the current series.

Airflow is an Apache Software Foundation project under the Apache-2.0 license and runs on your own infrastructure, from a single machine to Kubernetes. Several vendors offer managed Airflow services, but the project itself is self-hosted software. It suits data engineers who prefer code-first orchestration over drag-and-drop tools.

Kestra is an orchestration platform, open source, for data pipelines, AI workflows and infrastructure automation. It brings scheduled and event-triggered automation together under one declarative interface that does not depend on a programming language, applying infrastructure-as-code practices to pipelines so that reliable workflows can be defined in a few lines of YAML.

Workflows can be built in the UI, written by a built-in AI Copilot, or generated from coding agents such as Claude Code and Cursor using agent skills, and everything can be kept as code with Git integration even when it was authored visually. A large plugin ecosystem connects it to external systems, and key concepts cover tasks, triggers and flows. The project is written in Java, with topics covering orchestration, high availability, pipeline-as-code and DevOps, and the 2.0 release adds new capabilities.

Kestra is Apache-2.0 licensed and can be self-hosted, with a quick start that gets a first workflow running in minutes. It targets data engineers, platform teams and automation developers who want an alternative to schedulers such as Airflow with a more declarative style.

Prefect is a Python framework for orchestrating workflows and building data pipelines. It aims to be the simplest way to lift an ordinary script into a production workflow: you add flow and task decorators, and Prefect adds the machinery that makes pipelines dependable and visible.

That machinery includes scheduling, caching, retries and event-based automations, and flows can express dependencies and complex branching logic. The goal is dynamic pipelines that react to changes in the world and recover from unexpected failures. Run activity is tracked, and you can monitor it from a self-hosted Prefect server or from the managed Prefect Cloud dashboard. Prefect requires Python 3.10 or newer, and the documentation covers installation, quickstart, building and deploying workflows and agent setup.

Prefect is licensed under Apache-2.0. Topics list data engineering, ML ops and observability among its uses. It is aimed at data teams who prefer to write workflows as plain Python code, and who want a choice between running the orchestrator themselves or using a hosted control plane.

Dagster is an orchestration platform for developing, running and observing data assets. Instead of thinking only in terms of tasks, teams define the tables, files and models their pipelines produce, and Dagster tracks how they are built, scheduled and monitored. It is written in Python and aimed at data engineering, analytics and machine learning workloads.

The product surface covers data orchestration, a data catalog, data quality checks, cost insights and many integrations, with an enterprise offering for larger organizations. Comparison pages on the website position it against Airflow, dbt Cloud, Azure Data Factory and AWS Step Functions, and topics include ETL, scheduling and MLOps. The homepage now announces that Dagster is part of Prefect, and points visitors looking for agentic orchestration or MCP support to Prefect.

The open-source core is Apache-2.0 licensed and can be self-hosted, while a managed cloud and enterprise plans are offered with pricing on the company site. Dagster University and documentation help with learning. It suits data teams that want a software-engineering-style approach to pipelines.