# orchest/orchest

Build data pipelines, the easy way 🛠️

Repository: https://github.com/orchest/orchest
Canonical: https://ross.abutalabs.com/products/orchest
Homepage: https://orchest.readthedocs.io/en/stable/
Language: TypeScript
License: Apache-2.0
License Family: permissive
Topics: data-science, machine-learning, pipelines, ide, jupyter, cloud, self-hosted, jupyterlab, notebooks, docker, python, data-pipelines, orchest, deployment, kubernetes, airflow, dag, etl, etl-pipeline
Archived: true
Last push: 2023-06-06T09:48:26+00:00

## Health v2 (maintenance only)
Score: 10/100 (v2, computed 2026-09-02T17:46:02.011165+00:00)
- activity 0, release rhythm 8, longevity 100
- inputs: {"age_days": 2295, "days_push": 1184, "days_rel": null, "gap_med": null, "n_releases_24m": 0}
- flags: archived
- formula: round(0.45*activity + 0.35*rhythm + 0.20*longevity); archived -> min(score, 10)

## Adoption (not part of the score)
Stars 4132, forks 260 (observed 2026-08-28T04:08:36.105207+00:00)

## What it is
Orchest is a self-hosted, browser-based tool for visually building and running data pipelines using notebooks and scripts in Python, R, or Julia, with no YAML or framework code required. Development has been discontinued; the maintainers recommend Apache Airflow instead.

## Use cases
- build data pipelines visually without writing YAML
- run Jupyter notebooks as pipeline steps on a schedule
- orchestrate ETL jobs written in Python, R, or Julia
- self-host a lightweight Airflow alternative for data workflows
- train and compare machine learning models in reproducible pipelines
- run dbt or PySpark steps inside a data pipeline

## When to choose
- you want a visual, notebook-first pipeline builder you can self-host
- your team prefers writing plain Python/R/Julia over DAG definitions
- you need periodic scheduled runs of data processing jobs

## When to avoid
- you need a workflow orchestrator under active development and maintenance
- you require production-grade scheduling at scale - use Apache Airflow instead
- you need long-term support or community fixes for bugs

## Facets
- artifact type: application
- maturity: abandoned
- function: etl, workflow-automation, scheduling, data-science, machine-learning
- domain: data-science, machine-learning, self-hosted
- platform: self-hosted, python
- tags: data-pipelines, notebooks, jupyterlab, visual-pipeline-editor, workflow-orchestration, discontinued, data-engineering, automation, docker, kubernetes, web-server

## Member repositories
- orchest/orchest (main) score 10

## Provenance
- Observed fields: from GitHub, fetched 2026-08-28T04:08:36.105207+00:00.
- Health v2: computed from the inputs above; adoption is never an input.
- Inferred fields (summary, facets, guidance): AI-extracted, prompt v1, taxonomy v1, on 2026-08-29T18:23:02.825151+00:00, confidence not recorded.
  - readme: https://github.com/orchest/orchest (fetched 2026-08-28T04:08:36.105207+00:00, sha 2166c66b9501)
- Data as of 2026-08-30T08:39:29.467469+00:00.