Astronomer
Managed Apache Airflow service for orchestrating and monitoring data pipelines in the cloud
Orchest
Visual, code‑first data pipelines without YAML or frameworks
Build, version, and run Python, R, or Julia pipelines via a UI, notebooks, and Git, with flexible environments and service orchestration.
Overview
Overview
Orchest lets data scientists and ML engineers design end‑to‑end pipelines without writing YAML or learning a new DSL. Using a drag‑and‑drop UI, you can connect notebook cells or script files written in Python, R, or Julia, define dependencies, and run the workflow on demand or on a schedule.
Deployment & Extensibility
Pipelines run in isolated environments that can be backed by local Docker, Kubernetes, or the managed Orchest Cloud service. Long‑running services (e.g., databases or web servers) can be launched once per pipeline execution and accessed by subsequent steps. Projects are versioned with Git, enabling reproducible experiments. Although Orchest is in beta and no longer actively developed, it remains usable for prototyping and small‑scale production, with Apache Airflow suggested for larger, long‑term needs.
Highlights
Drag‑and‑drop UI for building pipelines
Write pipeline steps directly in Python, R, or Julia notebooks or scripts
Run jobs on demand or on a schedule with custom environments
Integrate long‑running services that persist across pipeline steps
Pros
- Intuitive visual interface reduces pipeline setup time
- Supports multiple languages without extra configuration
- Git integration enables version control of pipelines
- Flexible environment definition works on local, Docker, or Kubernetes
Considerations
- No longer actively maintained; future updates uncertain
- Limited community compared to Airflow or Prefect
- Beta status may contain bugs
- Lacks built‑in advanced scheduling or monitoring features
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Fit guide
Great for
- Data scientists who prefer notebook‑centric workflow
- Teams needing quick prototyping of ETL pipelines
- Projects that benefit from visual pipeline design
- Workloads that require temporary services during execution
Not ideal when
- Enterprises requiring long‑term vendor support
- Complex production workflows needing extensive plugins
- Organizations that rely on YAML‑based DAG definitions
- Use cases demanding advanced monitoring and alerting out‑of‑the‑box
How teams use it
Train and compare regression models
Iterate on multiple models within a single visual pipeline, tracking performance and code versions.
Run dbt transformations alongside Python
Combine SQL‑based dbt models with custom Python steps for hybrid data processing.
Execute PySpark jobs
Process large datasets using Spark within the orchestrated workflow, leveraging defined environments.
Connect to external databases via SQLAlchemy
Pull, transform, and load data from relational sources in a reproducible, version‑controlled pipeline.
Tech snapshot
TypeScript45%
Python45%
Go6%
Shell2%
SCSS1%
Dockerfile1%
Frequently asked questions
Is Orchest still actively maintained?
Development has stopped; the project is in beta and receives no new features, though it remains usable.
How can I deploy Orchest?
You can self‑host using Docker or Kubernetes, or use the managed Orchest Cloud service.
Which programming languages are supported?
Python, R, and Julia can be used directly in notebooks or scripts.
How does version control work?
Projects are linked to a Git repository, allowing you to commit pipeline code and track changes.
Is there a hosted option?
Yes, Orchest Cloud provides a managed environment for running pipelines without self‑hosting.
Project at a glance
Dormant- Stars
- 4,144
- Watchers
- 4,144
- Forks
- 265
LicenseApache-2.0
Repo age5 years old
Last commit3 years ago
Self-hostingSupported
Primary languageTypeScript
Last synced yesterday