Open-source Alteryx alternatives

Mage, called Mage OSS in its README, is a self-hosted development environment for building data pipelines. It is meant for teams that automate ETL tasks, design data flows or orchestrate transformations, and it presents the work in a notebook-style interface made of modular blocks of code.

Pipelines are written block by block in Python, SQL or R. Jobs can be run manually or on a schedule, including cron expressions, and prebuilt connectors reach databases, APIs and cloud storage. Debugging is visual, with logs, live data previews and execution you can follow step by step, and dbt models can be built and run inside Mage. Topics on the repository also reference Spark, reverse ETL and machine learning workloads.

Mage OSS is licensed under Apache-2.0 and installs with Docker, pip or conda, with no cloud account required. For larger deployments the vendor offers Mage Pro, a commercial platform that adds enterprise orchestration, collaboration and AI-assisted workflows.

Orange is a data mining and visualization toolbox aimed at both beginners and experienced analysts. Instead of writing code, users assemble analyses as workflows on a canvas, which hides the underlying mechanics and exposes intuitive concepts. The project's stated goal is that anyone who owns data should be able to explore it.

The toolbox covers interactive data visualization along with classic machine learning methods such as classification, regression, clustering, decision trees and random forests. It is written in Python on top of scikit-learn, NumPy, SciPy and pandas, and extra functionality is added through add-ons installed from the menu bar; developers can write their own widgets from an example template.

Windows and macOS users can download a standalone installer, while Linux users and developers can install with conda, pip or uv, and winget is available on Windows. The project is open source, welcomes new widgets and contributions, and has a community on Discord.

Duckle is an open-source ETL and ELT platform for teams that want their data pipelines running on their own infrastructure. You build pipelines on a visual canvas, in Python or in SQL, then ship the same file to your own server or cloud account, where a headless runner executes it on a schedule.

Pipelines compile to SQL on DuckDB and use all the cores available, so a larger machine runs them faster. The platform lists support for a large catalog of components, dbt, change data capture, data quality checks, reverse ETL and lineage, along with a web console, roles, an audit trail and an MCP server so AI agents such as Claude or Cursor can work with it. Each pipeline is a single file that can live in git.

Duckle is written in Rust and licensed under Apache-2.0, and it runs headless in Docker or on a plain server, with Kubernetes among its topics. It states there is no vendor cloud and no per-row billing. It is an independent project by SlothFlowLabs and is not affiliated with DuckDB Labs or MotherDuck. It suits data engineers who want a self-hosted alternative to managed pipeline services.