Build data pipelines, the easy way
No frameworks. No YAML. Just write Python and R code in Notebooks.
For a complete list of Orchest's features, check out the overview in our docs!
- Visually construct pipelines through our user-friendly UI.
- Code in Notebooks.
- Run any subset of a pipeline directly or on a cron-like schedule.
- Easily define your dependencies to run on any machine.
Who should use Orchest?
- Data Scientists who want to rapidly prototype.
- Data Scientists who like to work in Notebooks.
- Data Scientists who are looking to create pipelines through a visual interface instead of YAML.
NOTE: Orchest is in alpha.
For GPU support, language dependencies other than Python, and other installation methods, such as building from source, please refer to our installation docs.
NOTE: On Windows, Docker has to be configured to use WSL 2. Make sure to clone Orchest inside the Linux environment. For more info and installation steps for Docker with WSL 2 backend, please visit https://docs.docker.com/docker-for-windows/wsl/.
Linux, macOS and Windows
Alternatively, signup for the free-of-charge Orchest Cloud and get a fully configured Orchest instance out of the box!
git clone https://github.com/orchest/orchest.git && cd orchest ./orchest install # Start Orchest. ./orchest start
Now that you have installed Orchest, get started with our quickstart tutorial, check out pipelines made by your fellow users, or have a look at our video tutorials explaining some of Orchest's core concepts.
The software in this repository is licensed as follows:
- All content residing under the "orchest-sdk/" directory of this repository is licensed under the "Apache-2.0" license as defined in "orchest-sdk/LICENSE".
- Content outside of the above mentioned directory is available under the "AGPL-3.0" license.
Join our Slack to chat about Orchest, ask questions, and share tips.
Contributions are more than welcome! Please see our contributor guides for more details.