dagster

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

Dagster is a data pipeline orchestrator that lets you declare data assets as Python functions and automatically runs them at the right time to keep those assets up-to-date, with built-in lineage tracking and observability.

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

pip install dagster

uv add dagster

poetry add dagster

Installing dagster

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.

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])

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.

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

Evidence: dagster-1.13.17-py3-none-any.whl

Tags

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