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Home/Categories/Data & Analytics/Dagster
icon of Dagster

Dagster

Open source alternative to Databricks, Azure Data Factory and Google Cloud Composer

Dagster is an open source data pipeline orchestrator with an asset-centric model, built-in data catalog, and scheduling for AI and analytics workflows. Apache 2.0 licensed.

15.6K stars
Python
Apache-2.0
Active this week
Visit websiteGitHub repo
image of Dagster
Contents
  1. 01Who Dagster is for
  2. 02The problem it solves
  3. 03How it solves it
  4. 04Strengths and trade-offs
  5. 05Dagster vs alternatives
  6. 06Tech stack
  7. 07FAQ
  8. 08Similar open-source tools
TL;DR

Dagster is an Apache-2.0 data orchestration tool for building, observing, and operating data assets and pipelines. It replaces parts of Airflow, Google Cloud Composer, and managed workflow tools for data teams that want asset-aware orchestration, typed development patterns, and better local-to-production workflows.Apache-2.0 · Python · 15.6K stars · Active this week

who it's for

Who Dagster is for#

Data platform teams managing asset lineage

Dagster helps teams see which data assets exist, what produces them, and what downstream jobs depend on them. This fits warehouses, lakehouses, dbt projects, and ML feature pipelines.

Skip if:

Skip it if your workflow is only a few simple cron jobs with no data lineage or asset ownership problem.

Analytics engineers coordinating dbt and Python

Dagster can orchestrate dbt transformations alongside Python jobs, checks, and external resources. It gives analytics teams a way to operate data workflows as software.

Skip if:

Use a warehouse-native scheduler if all transformations already live in one managed SQL environment and orchestration needs are minimal.

tech stack · detected from GitHub

What it's built on#

Languages
PythonTypeScript
Frameworks
Next.jsReact
Tooling
Webpack
frequently asked

FAQ#

What is Dagster used for?
Is Dagster open source?
How does Dagster compare to Airflow?
also worth a look

Similar open-source tools#

Kestra

Kestra

Declarative workflow orchestration for data and DevOps teams

26.9KJavaApache-2.0

Repository

Stars
15.6K
Forks
2.2K
License
Apache-2.0
Latest
1.13.7
Last commit
2 days ago
Last verified
May 30, 2026
Repo
dagster-io/dagster ↗

Additional details

Language
Python
Open issues
2,666
Contributors
677
First release
2018

Categories

Data & AnalyticsAI & Machine LearningDevOps & CI/CD

Tags

Workflow OrchestrationWorkflow AutomationLLMOpsDeveloper ToolsMonitoringData Visualization
the problem

The problem it solves#

Data pipelines fail when teams can only see tasks instead of the data assets those tasks produce. Airflow-style DAGs can schedule jobs, but they often leave lineage, testing, asset ownership, and data quality checks scattered across notebooks, warehouse SQL, and monitoring tools.\u000A\u000AAs data teams support analytics, machine learning, and production AI features, they need orchestration that explains what data exists, how it updates, and what broke. A generic scheduler is not enough once data assets become part of product reliability.

how Dagster solves it

How it solves it#

Asset-aware orchestration

Dagster models data assets directly, not only tasks. Teams can understand dependencies between tables, files, models, and downstream products in the same system that runs the jobs.

Python development workflow

Pipelines are defined in Python with testable code, local development tools, and clear resource configuration. That fits engineering-heavy data teams that want version control and review around orchestration logic.

Observability for runs and assets

Dagster tracks runs, materializations, metadata, and failures so teams can debug which asset changed and why. This is more actionable than a task-only success or failure log.

Integrations for modern data stacks

Dagster integrates with warehouses, dbt, Spark, Kubernetes, cloud storage, and ML workflows. It can coordinate data engineering and analytics engineering in one orchestration layer.

strengths · trade-offs

Strengths and trade-offs#

Strengths

  • Better mental model for data productsAssets make Dagster easier to reason about when the real output is a table, feature set, dashboard input, or model artifact. That helps teams connect orchestration to business-facing data reliability.
  • Open source with managed optionThe core is Apache-2.0 licensed, and Dagster Cloud is available for teams that want hosted operations. Teams can start self-hosted and move to managed support if operations become the bottleneck.

Trade-offs

  • -Migration from Airflow takes redesignDagster's asset model is different from task-first DAGs. Teams migrating from Airflow should plan to rethink pipeline boundaries rather than mechanically port every operator.
  • -Python-centric workflowDagster is strongest for teams comfortable with Python-defined orchestration. Teams that want a visual-only low-code data pipeline tool may prefer managed ETL products.
versus alternatives

Dagster vs alternatives#

Dagster vs Airflow\u000A\u000ADagster and Airflow both orchestrate data workflows, but Dagster centers the data assets those workflows produce while Airflow centers scheduled tasks.\u000A\u000A| Criterion | Dagster | Airflow |\u000A| --- | --- | --- |\u000A| License | Apache-2.0 | Apache-2.0 |\u000A| Core model | Data assets and jobs | Task DAGs |\u000A| Development | Python with local tooling | Python DAG files and operators |\u000A| Best fit | Asset-aware data products | Broad scheduler ecosystem and legacy DAGs |\u000A\u000ADagster is the better choice when lineage, asset ownership, and data quality are central to the workflow. Airflow remains attractive when a team already has many DAGs, existing operators, and a mature Airflow operations practice.

Dagster models data assets and their dependencies, while Airflow traditionally focuses on task DAGs. Airflow has broader legacy adoption; Dagster is often a better fit for teams that want asset-aware data operations.

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Dagster is used to orchestrate data assets, pipelines, dbt jobs, ML workflows, and production data processes. It focuses on data lineage and asset observability, not just task scheduling.

Yes. Dagster's core project is Apache-2.0 licensed. Dagster Labs also offers Dagster Cloud for teams that want managed orchestration operations.