cube_dbt
dbt integration for Cube
Decision gist · record as of 2026-08-14
Yes, if you are actively using both dbt and Cube and want to automate the connection between them. The low install friction, permissive license, and clean API make it straightforward to adopt. However, the aging maintenance status (296 days since last release) means you should verify compatibility with your current dbt and Cube versions before relying on it in production, and be prepared for slower issue resolution if problems arise.AI-flagged interpretation of the facts on this page — verify before relying
Before you install
- Requires a dbt manifest.json file accessible via URL or local path; dbt project must be initialized and compiled before manifest is available.
- Low install friction with only two runtime dependencies (PyYAML, orjson).
- Maintenance status is aging—last release was 296 days ago—so expect slower response to issues or feature requests.
License · maintenance · safety
MIT (permissive) — MIT license is permissive; you can use, modify, and distribute this package freely with minimal restrictions, making it safe for commercial and open-source projects.
last release 2025-10-22 (296 days)
0 known vulnerabilities (OSV.dev, 2026-08-14) · 106,005 downloads/mo, #12,674 on PyPI
Alternatives
Verify before relying
pip install cube_dbt
from cube_dbt import Dbt
manifest_url = 'https://bucket.s3.amazonaws.com/manifest.json'
dbt = Dbt.from_url(manifest_url).filter(paths=['marts/'], tags=['cube'])
print(dbt.models)
print(dbt.model('name').as_cube())- Whether the package handles large manifests efficiently beyond the preprocessing guidance in the docs.
- Current state of the Cube.dev ecosystem and whether this integration remains actively used.
- Compatibility with recent dbt versions and Cube versions not explicitly stated in the fact sheet.
What it is and what it does
cube_dbt is a bridge between dbt and Cube's semantic layer. It reads dbt manifest files (the compiled output of a dbt project) and exposes dbt models and their metadata—names, descriptions, columns, tags, data types—in a form that Cube can consume to build its data model. The package provides a Python API to load a manifest from a URL, filter models by path or tag, and convert individual models or columns into Cube dimension and measure specifications.
Typically used in workflows where dbt defines the transformation layer and Cube defines the semantic/metrics layer on top. You load the manifest, filter to the models you want Cube to know about, and then call methods like `as_cube()` or `as_dimensions()` to generate Cube-compatible definitions. The package is lightweight—only PyYAML and orjson as dependencies—and supports Python 3.8 and later.
Use it for
- Automatically generate Cube data models from existing dbt projects without manual schema definition.
- Filter dbt models by path, tag, or name to expose only specific models to Cube's semantic layer.
- Extract column metadata (names, descriptions, data types) from dbt to populate Cube dimensions and measures.
- Preprocess large dbt manifests for performance optimization when working with complex projects.
- Integrate dbt and Cube in CI/CD pipelines to keep semantic definitions in sync with dbt transformations.
Worth the install?
AI-flagged interpretation of the facts on this page. Verify before relying on it.
Yes, if you are actively using both dbt and Cube and want to automate the connection between them.
The low install friction, permissive license, and clean API make it straightforward to adopt. However, the aging maintenance status (296 days since last release) means you should verify compatibility with your current dbt and Cube versions before relying on it in production, and be prepared for slower issue resolution if problems arise.
Install
cube-dbt on PyPI
Before you install
Low install friction with only two runtime dependencies (PyYAML, orjson). Maintenance status is aging—last release was 296 days ago—so expect slower response to issues or feature requests.
Requires a dbt manifest.json file accessible via URL or local path; dbt project must be initialized and compiled before manifest is available.
License in practice
MIT license is permissive; you can use, modify, and distribute this package freely with minimal restrictions, making it safe for commercial and open-source projects.
Quickstart
pip install cube_dbt
from cube_dbt import Dbt
manifest_url = 'https://bucket.s3.amazonaws.com/manifest.json'
dbt = Dbt.from_url(manifest_url).filter(paths=['marts/'], tags=['cube'])
print(dbt.models)
print(dbt.model('name').as_cube())Verify before relying
- Whether the package handles large manifests efficiently beyond the preprocessing guidance in the docs.
- Current state of the Cube.dev ecosystem and whether this integration remains actively used.
- Compatibility with recent dbt versions and Cube versions not explicitly stated in the fact sheet.
Package facts
| License | MIT permissive |
| Python support | Supports the current Python release >=3.8 |
| Install friction | Low. Pure-Python wheel |
| Runtime dependencies | 2 packagesPyYAMLorjson |
| Maintenance | Aging 296 days since the last release |
| First released | |
| Downloads | 106,005 / month, #12,674 on PyPI 30-day window, as of 2026-08-14 |
| Known vulnerabilities | None known OSV.dev, checked 2026-08-14 |
Evidence: cube_dbt-0.6.3-py3-none-any.whl
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