---
id: dagster-dg-core
version: "1.13.18"
license: Apache-2.0
license_treatment: permissive
maintenance: active
---

# dagster-dg-core

License: permissive · Maintenance: active · Downloads: 3.2M/mo

## What it is and what it does

dagster-dg-core is the foundational orchestration library for Dagster, a data pipeline platform built around declaring data assets as Python functions. You define what assets you want to build and their dependencies; Dagster handles scheduling, execution, and keeping them up-to-date. It integrates with a large ecosystem of data tools and provides built-in lineage tracking, observability, and testability across the full development lifecycle from local testing to production.

The package depends on 18 runtime libraries including click, jinja2, jsonschema, pyyaml, and rich, which provide CLI scaffolding, templating, configuration validation, and terminal formatting. It is designed to work at every stage—unit tests, integration tests, staging, and production—and supports Python 3.10 through 3.14. The project is actively maintained with recent releases and a large community presence.

Use it for:

- Define and orchestrate multi-stage data pipelines where downstream assets depend on upstream transformations.
- Build reusable, testable data components that can be unit-tested locally before deployment to production.
- Track data lineage and dependencies across a complex asset graph with integrated observability and diagnostics.
- Maintain data freshness by declaring asset definitions once and letting Dagster handle scheduling.
- Integrate with existing data tools and deploy to your own infrastructure using Dagster's integrations.

## Worth the install?

AI-flagged interpretation of the facts above — verify before relying.

dagster-dg-core provides the core orchestration and asset-definition framework for Dagster, enabling you to declare data assets as Python functions and manage their execution and dependencies.

Yes. dagster-dg-core is the core of an actively maintained, well-starred orchestration platform with permissive licensing and low install friction. Install it if you are building data pipelines in Python and want declarative asset management with built-in lineage and observability. If you are new to Dagster, verify whether you should install dagster itself or this package first by consulting the official documentation.

## Install

pip install dagster-dg-core
uv add dagster-dg-core
poetry add dagster-dg-core

## Installing dagster-dg-core

Before you install: Low install friction with a pure-wheel distribution. Active maintenance with a recent release and 15996 GitHub stars indicate a well-supported project.

License in practice: Apache-2.0 permissive license allows commercial use, modification, and distribution with minimal restrictions—suitable for most production and proprietary contexts.

Quickstart:

pip install dagster-dg-core

import dagster as dg
from jinja2 import Template

# Use Dagster's asset decorator and Jinja2 for templating
template = Template('Hello {{ name }}')
result = template.render(name='asset')

Requires Python 3.10 or later (up to 3.14); runtime dependencies must be installed separately.

Verify before relying:

- Whether dagster-dg-core is the correct entry point or if dagster itself should be installed instead for typical use cases.
- Specific performance characteristics and scalability limits for large asset graphs or high-frequency scheduling.
- Integration requirements with dagster-cloud-cli and other Dagster ecosystem packages for production deployments.
- What data asset types and transformations the package supports beyond the examples in the description.

## Package facts

- License: Apache-2.0 (permissive)
- Python support: supports_current
- Install friction: low
- Maintenance: active
- Downloads: 3.2M/month (top 5,000 on PyPI)
- Known vulnerabilities: none known

## Tags

data pipeline orchestration, asset-based data workflows, declarative data asset management, python data orchestration, data lineage and observability, dagster core framework, data pipeline testing, data-orchestration, asset-management, pipeline-framework

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