dagster
Dagster is an orchestration platform for the development, production, and observation of data assets.
Decision gist · record as of 2026-08-14
Yes. Dagster is a mature, actively maintained orchestration platform (released 7 days ago, production-stable) with low install friction, permissive licensing, and no known vulnerabilities. It is well-suited for teams building data pipelines of any scale, from local development to production. The large dependency footprint is pre-packaged, so installation is straightforward. Install it if you need declarative asset-based orchestration with integrated lineage and observability; skip it if you prefer lightweight task scheduling or are committed to a different orchestration paradigm.AI-flagged interpretation of the facts on this page — verify before relying
Before you install
- Requires Python 3.10 or later (supports 3.10 through 3.14); for the web UI and CLI, install dagster-webserver and dagster-dg-cli separately.
- Low install friction with a pure-Python wheel and 28 runtime dependencies already packaged.
- Active maintenance—released 7 days ago with the last commit on 2026-08-13—and production-stable status (Development Status :: 5) suggest a mature, well-supported project.
License · maintenance · safety
Apache-2.0 (permissive) — Apache-2.0 permissive license allows commercial use, modification, and distribution with minimal restrictions, making it suitable for proprietary projects and enterprise deployments.
last release 2026-08-07 (7 days) · last repo commit 2026-08-13 · 15,996 stars
0 known vulnerabilities (OSV.dev, 2026-08-14) · 9,669,960 downloads/mo, #1,508 on PyPI
Alternatives
Verify before relying
pip install dagster
import dagster as dg
@dg.asset
def my_asset():
return None
if __name__ == "__main__":
dg.materialize([my_asset])- Whether the 28 runtime dependencies introduce significant transitive dependency bloat in production environments.
- Performance characteristics and resource overhead when orchestrating large numbers of assets or complex DAGs.
- Specific cloud deployment targets and whether all integrations are included in the base package or require separate installation.
What it is and what it does
Dagster is a cloud-native orchestration platform designed to manage the full lifecycle of data assets—tables, datasets, machine learning models, reports—from local development through production. You define assets as Python functions decorated with @dg.asset, declare their dependencies, and Dagster handles scheduling, execution, and keeping them up-to-date. It provides a declarative programming model, integrated lineage tracking, and observability built in, so you can see what data flows where and spot issues early.
The platform scales from solo development (with local testing and unit test support) to production deployments with multi-tenant orchestration, centralized metadata management, and diagnostics. It ships with a web UI for monitoring and a CLI for management, and integrates with popular data tools across the modern data stack. The fact sheet shows active maintenance (released 7 days ago), production-stable status, and a large runtime dependency footprint (28 packages) that is already bundled, so installation is straightforward.
Use it for
- Build and schedule ETL pipelines that transform raw data into analytics-ready tables, with automatic dependency resolution and failure handling.
- Develop machine learning workflows where training datasets, model artifacts, and predictions are tracked as versioned assets with full lineage.
- Monitor data quality and catch upstream issues early by defining asset dependencies and running tests at each stage of the pipeline.
- Orchestrate complex multi-step analytics reports that depend on multiple upstream data sources and need to run on a schedule.
- Manage data assets across teams in a centralized control plane with observability, cataloging, and role-based access.
- Test data pipelines locally during development, then promote the same code to staging and production without rewriting.
Worth the install?
AI-flagged interpretation of the facts on this page. Verify before relying on it.
Yes.
Dagster is a mature, actively maintained orchestration platform (released 7 days ago, production-stable) with low install friction, permissive licensing, and no known vulnerabilities. It is well-suited for teams building data pipelines of any scale, from local development to production. The large dependency footprint is pre-packaged, so installation is straightforward. Install it if you need declarative asset-based orchestration with integrated lineage and observability; skip it if you prefer lightweight task scheduling or are committed to a different orchestration paradigm.
Install
dagster on PyPI
Before you install
Low install friction with a pure-Python wheel and 28 runtime dependencies already packaged. Active maintenance—released 7 days ago with the last commit on 2026-08-13—and production-stable status (Development Status :: 5) suggest a mature, well-supported project.
Requires Python 3.10 or later (supports 3.10 through 3.14); for the web UI and CLI, install dagster-webserver and dagster-dg-cli separately.
License in practice
Apache-2.0 permissive license allows commercial use, modification, and distribution with minimal restrictions, making it suitable for proprietary projects and enterprise deployments.
Quickstart
pip install dagster
import dagster as dg
@dg.asset
def my_asset():
return None
if __name__ == "__main__":
dg.materialize([my_asset])Verify before relying
- Whether the 28 runtime dependencies introduce significant transitive dependency bloat in production environments.
- Performance characteristics and resource overhead when orchestrating large numbers of assets or complex DAGs.
- Specific cloud deployment targets and whether all integrations are included in the base package or require separate installation.
Package facts
| License | Apache-2.0 permissive |
| Python support | Supports the current Python release <3.15,>=3.10 |
| Install friction | Low. Pure-Python wheel |
| Runtime dependencies | 28 packagesalembicantlr4-python3-runtimeclickcoloredlogsdagster-pipesdagster-shareddocstring-parserfilelockgrpcio-health-checkinggrpciojinja2protobufpsutilpython-dotenvpytzpywin32requestsrichsixsqlalchemystructlogtabulatetomlitoposorttqdmtzdatauniversal-pathlibwatchdog |
| Maintenance | Actively maintained 7 days since the last release |
| Last repo commit | |
| First released | |
| Downloads | 9,669,960 / month, #1,508 on PyPI 30-day window, as of 2026-08-14 |
| Known vulnerabilities | None known OSV.dev, checked 2026-08-14 |
| Classifiers | Development Status :: 5 - Production/StableEnvironment :: ConsoleEnvironment :: Web EnvironmentIntended Audience :: DevelopersIntended Audience :: System AdministratorsTopic :: Software Development :: Libraries :: Application FrameworksTopic :: System :: Monitoring |
Evidence: dagster-1.13.17-py3-none-any.whl
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See also dagster-airbyte · dagster-aws · dagster-azure · dagster-databricks · dagster-datadog · dagster-dbt · dagster-dg-cli · dagster-dg-core · dagster-docker · dagster-duckdb