specdbt
BDD-style Given/When/Then testing for dbt models and macros. Write a
scenario a stakeholder can read; specdbt compiles and runs it for real
against dbt and reports pass/fail — no hand-maintained YAML, no
hardcoded expected output.
Feature: stg_customers renames the raw seed's id column
Scenario: Renames id to customer_id, passes names through unchanged
Given the following rows in "raw_customers":
| id | first_name | last_name |
| 1 | Michael | P. |
When the "stg_customers" model runs
Then the "stg_customers" should produce the following rows:
| customer_id | first_name | last_name |
| 1 | Michael | P. |
Models compile to a real dbt unit_tests: entry and run via dbt test.
Macros — which dbt has no native way to unit test — run
through a real ephemeral-table/dbt show pipeline instead. Any warehouse
dbt-core supports is in scope; DuckDB and Postgres are CI-verified,
Databricks is manually validated (see docs).
Install
uv add --dev specdbt # or: pip install specdbt
Add your warehouse's dbt adapter alongside it, e.g. uv add --dev "specdbt[postgres]" (databricks, snowflake extras also available;
DuckDB ships built in).
Quickstart
uv add --dev specdbt
uv run specdbt init features/ # scaffold an example .feature file
uv run specdbt run features/ \
--engine dbt --project-dir . --target <your target>
Docs
docs/knowledge/index.md— architecture, adapters, the two-tier model/macro design.docs/knowledge/gherkin-style-guide.md— how to write scenarios.examples/jaffle_shop/— a full worked example (just run-example).CONTRIBUTING.md— local setup, testing other adapters, release process.
License
MIT — see LICENSE.
1 maintainer
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