dbt-bouncer
Configure and enforce conventions for your dbt project.
Decision gist · record as of 2026-08-14
Yes. dbt-bouncer is actively maintained, has low install friction, carries no known vulnerabilities, and solves a real problem for teams using dbt—enforcing conventions at scale. The MIT license is permissive. Install it if your team uses dbt and wants to standardize project structure and naming without manual code review.AI-flagged interpretation of the facts on this page — verify before relying
Before you install
- Requires a dbt project with a manifest.json file (generated by `dbt parse`) and a dbt-bouncer.yml config file.
- Low install friction with a pure Python wheel and 12 well-established runtime dependencies.
- Active maintenance with a release 28 days ago; supports current Python versions (3.11–3.14).
License · maintenance · safety
MIT (permissive) — MIT license permits unrestricted use, modification, and distribution with minimal legal obligations.
last release 2026-07-17 (28 days) · last repo commit 2026-08-13 · 125 stars
0 known vulnerabilities (OSV.dev, 2026-08-14) · 92,499 downloads/mo, #13,450 on PyPI
Alternatives
Verify before relying
pip install dbt-bouncer
dbt parse
dbt-bouncer init
dbt-bouncer run- Whether the package supports dbt versions other than the latest; compatibility matrix not provided in fact sheet.
- Performance characteristics when running against large dbt projects with many models and tests.
- Whether custom check rules can be written or if only built-in checks are available.
What it is and what it does
dbt-bouncer is a CLI tool that enforces naming conventions and project structure rules on dbt projects by analyzing dbt artifacts (manifest.json, catalog.json, run_results.json). It reads a YAML configuration file defining checks—such as model naming patterns, documentation requirements, or test coverage—then runs those checks against your dbt project and reports violations with severity levels.
The tool integrates into dbt workflows and CI/CD pipelines to catch convention violations early. It depends on Jinja2 for templating, Pydantic for configuration validation, sqlglot for SQL parsing, and rich for formatted terminal output. Configuration is declarative and human-readable, making it accessible for teams to define and maintain their own dbt standards without writing code.
Use it for
- Enforce naming conventions (e.g., staging models start with 'stg_') across a dbt project in CI/CD.
- Validate that all models have documentation and tests before deployment.
- Check for consistency in model structure, column naming, or tagging across a team's dbt codebase.
- Prevent common dbt anti-patterns by defining project-specific rules in a config file.
- Generate a summary report of dbt project structure and check results for governance audits.
Worth the install?
AI-flagged interpretation of the facts on this page. Verify before relying on it.
Yes.
dbt-bouncer is actively maintained, has low install friction, carries no known vulnerabilities, and solves a real problem for teams using dbt—enforcing conventions at scale. The MIT license is permissive. Install it if your team uses dbt and wants to standardize project structure and naming without manual code review.
Install
dbt-bouncer on PyPI
Before you install
Low install friction with a pure Python wheel and 12 well-established runtime dependencies. Active maintenance with a release 28 days ago; supports current Python versions (3.11–3.14).
Requires a dbt project with a manifest.json file (generated by `dbt parse`) and a dbt-bouncer.yml config file.
License in practice
MIT license permits unrestricted use, modification, and distribution with minimal legal obligations.
Quickstart
pip install dbt-bouncer
dbt parse
dbt-bouncer init
dbt-bouncer runVerify before relying
- Whether the package supports dbt versions other than the latest; compatibility matrix not provided in fact sheet.
- Performance characteristics when running against large dbt projects with many models and tests.
- Whether custom check rules can be written or if only built-in checks are available.
Package facts
| License | MIT permissive |
| Python support | Supports the current Python release <3.15,>=3.11 |
| Install friction | Low. Pure-Python wheel |
| Runtime dependencies | 12 packagesjellyfishjinja2-simple-tagsjinja2junitparserorjsonpackagingpydanticpyyamlrichsemversqlglottyper |
| Maintenance | Actively maintained 28 days since the last release |
| Last repo commit | |
| First released | |
| Downloads | 92,499 / month, #13,450 on PyPI 30-day window, as of 2026-08-14 |
| Known vulnerabilities | None known OSV.dev, checked 2026-08-14 |
| Classifiers | Programming Language :: PythonProgramming Language :: Python :: 3Programming Language :: Python :: 3 :: OnlyProgramming Language :: Python :: 3.11Programming Language :: Python :: 3.12Programming Language :: Python :: 3.13Programming Language :: Python :: 3.14 |
Evidence: dbt_bouncer-3.8.0-py3-none-any.whl
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See also collate-dbt-artifacts-parser · dbt-score · dbt-artifacts-parser · dbt-coverage · dbt-loom · cube_dbt · dbt-metabase · dbt-semantic-interfaces · dbt-colibri · dbt-autofix