dagster-dbt
A Dagster integration for dbt
Decision gist · record as of 2026-08-14
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.AI-flagged interpretation of the facts on this page — verify before relying
Before you install
- Requires a dbt project directory and dbt-core to be installed; Python 3.10 or later.
- Low friction install with a pure Python wheel.
- Active maintenance with a recent release and a large community (15996 GitHub stars).
License · maintenance · safety
Apache-2.0 (permissive) — Apache-2.0 permissive license allows commercial and private use without restriction, making it suitable for both open-source and proprietary projects.
last release 2026-08-14 (0 days) · last repo commit 2026-08-13 · 15,996 stars
0 known vulnerabilities (OSV.dev, 2026-08-14) · 2,681,013 downloads/mo, #2,944 on PyPI
Alternatives
Verify before relying
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()- 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
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 on it.
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
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.
Requires a dbt project directory and dbt-core to be installed; Python 3.10 or later.
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()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
| License | Apache-2.0 permissive |
| Python support | Supports the current Python release <3.14,>=3.10 |
| Install friction | Low. Pure-Python wheel |
| Runtime dependencies | 11 packagesdagsterdbt-coregitpythonjinja2networkxorjsonpackagingrequestsrichsqlglottyper |
| Maintenance | Actively maintained 0 days since the last release |
| Last repo commit | |
| First released | |
| Downloads | 2,681,013 / month, #2,944 on PyPI 30-day window, as of 2026-08-14 |
| Known vulnerabilities | None known OSV.dev, checked 2026-08-14 |
Evidence: dagster_dbt-0.29.18-py3-none-any.whl
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See also dagster · dagster-mlflow · elementary-data · dagster-cloud-cli · dagster-pandera · dagster-fivetran · dagster-duckdb · acryl-datahub-dagster-plugin · dagster-dg-core · dagster-webserver