Notice: we’re no longer actively developing Orchest. We could not find a way to make building a workflow orchestrator commercially viable. Check out Apache Airflow for a robust workflow solution.
Build data pipelines, the easy way 🙌
No frameworks. No YAML. Just write your data processing code directly in Python, R or Julia.
💡 Watch the full narrated video to learn more about building data pipelines in Orchest.
Note: Orchest is in beta.
- Visually construct pipelines through our user-friendly UI
- Code in Notebooks and scripts (quickstart)
- Run any subset of a pipelines directly or periodically (jobs)
- Easily define your dependencies to run on any machine (environments)
- Spin up services whose lifetime spans across the entire pipeline run (services)
- Version your projects using git (projects)
When to use Orchest? Read it in the docs.
Get started with an example project:
- Train and compare 3 regression models
- Connecting to an external database using SQLAlchemy
- Run dbt in Orchest for a dbt + Python transform pipeline
- Use PySpark in Orchest
👉 Check out the full list of example projects.
Join our Slack to chat about Orchest, ask questions, and share tips.
The software in this repository is licensed as follows:
- All content residing under the
orchest-cli/directories of this repository are licensed under the
Apache-2.0license as defined in
- Content outside of the above mentioned directories is available under the
Contributions are more than welcome! Please see our contributor guides for more details.
Alternatively, you can submit your pipeline to the curated list of Orchest examples that are automatically loaded in every Orchest deployment! 🔥