dagster-dbt

What it is and what it does

dagster-dbt is a Dagster integration that bridges dbt (a data transformation tool) with Dagster's orchestration and observability platform. It allows you to represent dbt models, tests, and snapshots as Dagster assets, enabling you to compose them into larger data pipelines alongside other Python-based computations, SQL queries, and machine learning workflows. The integration handles scheduling, execution, and monitoring of dbt runs within Dagster's unified control plane.

The package depends on dagster and dbt-core, along with supporting libraries for parsing, networking, and data handling (gitpython, jinja2, networkx, orjson, packaging, requests, rich, sqlglot, typer). It is designed for teams building modern data stacks who want to orchestrate dbt workflows alongside other data assets in a single, observable system rather than managing dbt separately.

Use it for:

• Orchestrate dbt models as part of a larger Dagster data asset graph that includes Python transformations and ML models
• Monitor dbt test results and model freshness within Dagster's lineage and observability interface
• Schedule dbt runs on a defined cadence and trigger them based on upstream data asset changes
• Build reusable dbt asset definitions that can be composed across multiple projects or environments
• Integrate dbt transformations with data quality checks and alerting in a centralized control plane

Worth the install?

AI-flagged interpretation of the facts on this page — verify before relying

Integrates dbt data transformation workflows into Dagster's orchestration platform, allowing you to define, schedule, and monitor dbt models as part of a larger data asset graph.

Yes. The package is actively maintained, has no known vulnerabilities, and low install friction. It is the standard way to integrate dbt into Dagster workflows. Install it if you are using Dagster for orchestration and want to include dbt models in your asset graph; skip it if you are managing dbt independently or not using Dagster.

Install

dagster-dbt on PyPI

pip install dagster-dbt

uv add dagster-dbt

poetry add dagster-dbt

Installing dagster-dbt

Before you install

Low friction install with a pure Python wheel. Active maintenance with a recent release and a large community (15996 GitHub stars). Requires dagster and dbt-core as runtime dependencies, plus a standard set of data-processing libraries.

License in practice

Apache-2.0 permissive license allows commercial and private use without restriction, making it suitable for both open-source and proprietary projects.

Quickstart

pip install dagster-dbt

import dagster as dg
from dagster_dbt import DbtProject

project = DbtProject(project_dir="./my_dbt_project")
dbt_assets = project.build_dbt_assets()

Requires a dbt project directory and dbt-core to be installed; Python 3.10 or later.

Verify before relying

• Whether the integration supports all dbt adapters or only a subset
• Performance characteristics when orchestrating large numbers of dbt models
• How dbt test results are surfaced in Dagster's observability layer

Package facts

Evidence: dagster_dbt-0.29.18-py3-none-any.whl

Tags

More Distributed Computing packages