---
id: dbt-bouncer
version: "3.8.0"
license: MIT
license_treatment: permissive
maintenance: active
---

# dbt-bouncer — Configure and enforce conventions for your dbt project.

License: permissive · Maintenance: active · Downloads: 92.5K/mo

## 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 above — verify before relying.

dbt-bouncer validates dbt projects against configurable conventions and naming rules, running checks on dbt artifacts to enforce project-wide standards.

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

pip install dbt-bouncer
uv add dbt-bouncer
poetry add dbt-bouncer

## Installing dbt-bouncer

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).

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 run

Requires a dbt project with a manifest.json file (generated by `dbt parse`) and a dbt-bouncer.yml config file.

Verify 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_current
- Install friction: low
- Maintenance: active
- Downloads: 92.5K/month (top 15,000 on PyPI)
- Known vulnerabilities: none known

## Tags

dbt linting and validation, dbt project conventions, dbt naming rules enforcement, dbt code quality checks, dbt manifest validation, dbt best practices, dbt CI/CD validation, dbt, linting, ci-cd

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