# apache/dolphinscheduler

Apache DolphinScheduler is the modern data orchestration platform. Agile to create high performance workflow with low-code

Repository: https://github.com/apache/dolphinscheduler
Canonical: https://ross.abutalabs.com/products/dolphinscheduler
Homepage: https://dolphinscheduler.apache.org/
Language: Java
License: Apache-2.0
License Family: permissive
Topics: workflow-schedule, azkaban, airflow, task-scheduler, job-scheduler, cloud-native, data-pipelines, orchestration, workflow, workflow-orchestration, powerful-data-pipelines
Last push: 2026-08-26T08:18:37+00:00

## Health v2 (maintenance only)
Score: 90/100 (v2, computed 2026-09-02T17:46:02.011165+00:00)
- activity 99, release rhythm 74, longevity 100
- inputs: {"age_days": 2742, "days_push": 7, "days_rel": 95, "gap_med": 85, "n_releases_24m": 6}
- flags: none
- formula: round(0.45*activity + 0.35*rhythm + 0.20*longevity); archived -> min(score, 10)

## Adoption (not part of the score)
Stars 14447, forks 5084 (observed 2026-08-28T04:11:07.035984+00:00)

## What it is
Apache DolphinScheduler is a modern data orchestration platform for building high-performance workflows with low-code drag-and-drop tooling, a Python SDK, or Open API. It features a decentralized multi-master/multi-worker architecture with high availability, horizontal scaling, and support for deployment on standalone, Docker, and Kubernetes environments.

## Use cases
- orchestrate complex data pipelines with task dependencies
- schedule and monitor batch ETL jobs
- replace Airflow or Azkaban with a low-code workflow scheduler
- run workflows across multiple clouds and data centers
- manage multi-tenant job scheduling with permission control
- backfill and version-control workflow runs
- deploy a high-availability task scheduler on Kubernetes

## When to choose
- you need a visual, low-code DAG builder for data pipelines
- you require tens of millions of tasks per day with horizontal scaling
- you want decentralized multi-master/multi-worker high availability out of the box
- you need multi-cloud, multi-tenancy, and fine-grained permission control
- you prefer a Web UI, Python SDK, or Open API for workflow management

## When to avoid
- your team is already invested in Airflow's Python-native DAG ecosystem
- you only need simple cron-style scheduling without complex dependencies
- you want a lightweight single-node scheduler with minimal infrastructure
- your workflows are tightly coupled to a platform-specific orchestrator like AWS Step Functions

## Facets
- artifact type: application
- maturity: stable
- function: workflow-automation, scheduling, etl, deployment, web-framework
- domain: big-data, cloud-computing, self-hosted
- platform: jvm, self-hosted, cloud
- tags: workflow-orchestration, data-pipelines, task-scheduler, low-code, airflow-alternative, azkaban-alternative, multi-tenancy, distributed-systems, data-engineering, automation, docker, kubernetes, web-server

## Member repositories
- apache/dolphinscheduler (main) score 90

## Provenance
- Observed fields: from GitHub, fetched 2026-08-28T04:11:07.035984+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 2026-08-29T17:12:33.715775+00:00, confidence not recorded.
  - readme: https://github.com/apache/dolphinscheduler (fetched 2026-08-28T04:11:07.035984+00:00, sha b3575f35889e)
  - homepage: https://dolphinscheduler.apache.org/ (fetched 2026-08-29T08:05:55.494666+00:00, sha 3abdbeccc155)
- Data as of 2026-08-30T08:39:29.467469+00:00.