# dagster-io/dagster

An orchestration platform for the development, production, and observation of data assets.

Repository: https://github.com/dagster-io/dagster
Canonical: https://ross.abutalabs.com/products/dagster
Homepage: https://dagster.io
Language: Python
License: Apache-2.0
License Family: permissive
Topics: data-pipelines, dagster, workflow, data-science, workflow-automation, python, scheduler, data-orchestrator, etl, analytics, data-engineering, mlops, orchestration, data-integration, metadata
Last push: 2026-08-26T19:38:00+00:00

## Health v2 (maintenance only)
Score: 95/100 (v2, computed 2026-09-03T02:39:23.370411+00:00)
- activity 99, release rhythm 86, longevity 100
- inputs: {"age_days": 3047, "days_push": 7, "days_rel": 12, "gap_med": 7, "n_releases_24m": 104}
- flags: none
- formula: round(0.45*activity + 0.35*rhythm + 0.20*longevity); archived -> min(score, 10)

## Adoption (not part of the score)
Stars 16067, forks 2262 (observed 2026-08-28T04:11:14.776746+00:00)

## What it is
Dagster is an open-source Python data orchestration platform for developing, producing, and observing data assets, with integrated lineage, scheduling, and observability. It provides a declarative asset-based programming model, a web UI, a CLI, and a managed cloud offering (Dagster+).

## Use cases
- orchestrate etl pipelines with dependencies and scheduling
- build and monitor data assets with lineage and freshness tracking
- schedule and backfill dbt model runs
- automate machine learning pipelines and mlops workflows
- observe data pipeline health and failures from a ui
- migrate airflow dags to an asset-based orchestrator

## When to choose
- you need asset-centric orchestration with lineage, observability, and testability in Python
- you want to schedule and monitor dbt, Airbyte, Spark, or warehouse pipelines in one platform
- your team wants a modern alternative to Airflow with a strong developer experience

## When to avoid
- you need a lightweight cron-style scheduler without a platform or UI
- your workflows are not data-centric and a simple task queue suffices
- you cannot adopt Python as your orchestration language

## Facets
- artifact type: framework
- maturity: active
- function: workflow-automation, etl, scheduling, monitoring, data-science, developer-tools, cli, web-framework
- domain: data-science, analytics, big-data, machine-learning, developer-tools
- platform: python, cloud, self-hosted, cross-platform
- tags: data-orchestration, data-pipelines, assets, lineage, scheduler, mlops, elt, observability, dagster, declarative-pipelines, data-engineering, automation, docker, kubernetes

## Member repositories
- dagster-io/dagster (main) score 95

## Provenance
- Observed fields: from GitHub, fetched 2026-08-28T04:11:14.776746+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 2026-08-29T17:05:14.872299+00:00, confidence not recorded.
  - readme: https://github.com/dagster-io/dagster (fetched 2026-08-28T04:11:14.776746+00:00, sha edd1cb00df32)
  - homepage: https://dagster.io (fetched 2026-08-29T08:03:04.719385+00:00, sha 0a3974607a9b)
  - site_page: https://docs.dagster.io/ (fetched 2026-08-29T08:03:04.732817+00:00, sha 49b27ad3469d)
  - site_page: https://docs.dagster.io/getting-started/quickstart (fetched 2026-08-29T08:03:04.734481+00:00, sha 3bc387ffbe2c)
  - site_page: https://dagster.io/integrations (fetched 2026-08-29T08:03:04.728640+00:00, sha f6a5c5ade536)
  - site_page: https://dagster.io/pricing (fetched 2026-08-29T08:03:04.730851+00:00, sha c93b076c14d9)
- Data as of 2026-08-30T08:39:29.467469+00:00.