dagster-gcp
Package for GCP-specific Dagster framework op and resource components.
Decision gist · record as of 2026-08-14
Yes, if you are already using Dagster and need to orchestrate work on GCP. The package is actively maintained, has no known vulnerabilities, and low install friction. It is a natural extension of Dagster for teams committed to that orchestration model. If you are not yet using Dagster, evaluate the core platform first—this is an add-on, not a standalone tool.AI-flagged interpretation of the facts on this page — verify before relying
Before you install
- Requires Python 3.10 or later (up to 3.14).
- GCP credentials must be configured in your environment.
- Low install friction with a wheel distribution.
License · maintenance · safety
Apache-2.0 (permissive) — Apache 2.0 licensed, permissive terms allow commercial and private use with minimal restrictions.
last release 2026-08-14 (0 days) · last repo commit 2026-08-14 · 15,996 stars
0 known vulnerabilities (OSV.dev, 2026-08-14) · 1,164,979 downloads/mo, #4,278 on PyPI
Alternatives
Verify before relying
pip install dagster-gcp
import dagster as dg
from dagster_gcp import BigQueryResource
@dg.asset
def my_asset(bigquery: BigQueryResource):
return bigquery.get_client().query("SELECT").result()- Specific BigQuery dataset/table operations supported beyond basic client access
- Whether Cloud Storage integration includes direct read/write asset I/O or only resource provisioning
- Performance characteristics when orchestrating large-scale GCP workloads
What it is and what it does
dagster-gcp is a library that extends Dagster's orchestration capabilities with first-class support for Google Cloud Platform services. It provides resources and integrations for BigQuery, Cloud Storage, and other GCP tools, allowing you to declare data assets that live in or depend on GCP infrastructure and have Dagster manage their execution and lineage.
The package sits between Dagster's core orchestration engine and the GCP Python client libraries (google-cloud-bigquery, google-cloud-storage, google-api-python-client). It abstracts away credential management and connection pooling, letting you focus on defining what data assets you need rather than how to connect to them. It is part of the broader Dagster ecosystem and shares the same declarative, asset-oriented programming model.
Use it for
- Build and orchestrate data transformation pipelines that read from or write to BigQuery tables
- Manage machine learning model training workflows that use GCP compute and store results in Cloud Storage
- Centralize lineage and observability for data assets distributed across multiple GCP projects
- Integrate GCP data sources into a multi-cloud or hybrid data platform using Dagster as the control plane
Worth the install?
AI-flagged interpretation of the facts on this page. Verify before relying on it.
Yes, if you are already using Dagster and need to orchestrate work on GCP.
The package is actively maintained, has no known vulnerabilities, and low install friction. It is a natural extension of Dagster for teams committed to that orchestration model. If you are not yet using Dagster, evaluate the core platform first—this is an add-on, not a standalone tool.
Install
dagster-gcp on PyPI
Before you install
Low install friction with a wheel distribution. Actively maintained with recent releases. Depends on core Dagster plus GCP client libraries, all standard packages.
Requires Python 3.10 or later (up to 3.14). GCP credentials must be configured in your environment.
License in practice
Apache 2.0 licensed, permissive terms allow commercial and private use with minimal restrictions.
Quickstart
pip install dagster-gcp
import dagster as dg
from dagster_gcp import BigQueryResource
@dg.asset
def my_asset(bigquery: BigQueryResource):
return bigquery.get_client().query("SELECT").result()Verify before relying
- Specific BigQuery dataset/table operations supported beyond basic client access
- Whether Cloud Storage integration includes direct read/write asset I/O or only resource provisioning
- Performance characteristics when orchestrating large-scale GCP workloads
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 | 7 packagesdagster-pandasdagsterdb-dtypesgoogle-api-python-clientgoogle-cloud-bigquerygoogle-cloud-storageoauth2client |
| Maintenance | Actively maintained 0 days since the last release |
| Last repo commit | |
| First released | |
| Downloads | 1,164,979 / month, #4,278 on PyPI 30-day window, as of 2026-08-14 |
| Known vulnerabilities | None known OSV.dev, checked 2026-08-14 |
Evidence: dagster_gcp-0.29.18-py3-none-any.whl
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See also dagster · dagster-cloud-cli · dagster-aws · dagster-azure · dagster-webserver · dagster-dg-core · dagster-docker · dagster-graphql · dagster-rest-resources