dataforge-07-dbt 0.1.0

dbt relation-export wrapper for DataForge data-quality repair audits.

dataforge-dbt

Run DataForge checks from a dbt model post-hook and keep the audit artifacts in your dbt target/ directory.

python -m pip install -e ".[dev]"

Quick start

1. Install the Python dependency

The PyPI package is not published yet. Install from this source checkout:

python -m pip install -e ".[dev]"

After PyPI publication, pip install dataforge_07_dbt will install the integration plus the supported dbt-duckdb adapter path for the free local integration-test warehouse.

2. Add the dbt package

In packages.yml:

packages:
  - package: dataforge/dataforge
    version: 0.1.0

During local development from a checkout, use:

packages:
  - local: ../dataforge-dbt

Then run:

dbt deps

3. Add a model post-hook

{{ config(post_hook="{{ dataforge.dataforge_repair('column_x', mode='dry_run') }}") }}

select *
from {{ ref('my_source_model') }}

Supported modes:

• dry_run: logs proposed DataForge findings as warnings and writes no transaction artifact.
• apply: writes a JSONL audit artifact under target/dataforge_txns/.
• refuse: fails the hook if DataForge detects any UNSAFE issue.

4. Run dbt through the dispatcher

dataforge-dbt \
  --relation main.my_model \
  --column column_x \
  --mode dry_run \
  --target-path target \
  --project-dir . \
  --run-dbt

The macro emits a stable log line showing the dispatcher command for the relation and column. The Python dispatcher is the execution boundary: it can run dbt, export the relation through the adapter, run DataForge detectors, and write audit artifacts under target/. The macro itself remains SQL-only because dbt post-hooks do not provide a portable, safe shell execution surface.

5. Verify transaction artifacts

For mode='apply', verify that the target directory contains a DataForge transaction artifact:

ls target/dataforge_txns/

Each file is JSONL and includes the dbt relation, inspected column, UTC timestamp, and serialized DataForge issues.

Python dispatch entrypoint

The package also ships a Python command used by tests and automation:

dataforge-dbt \
  --relation main.example_with_dirty_data \
  --column column_x \
  --mode dry_run \
  --input-csv integration_tests/dbt_project/seeds/dirty_decimal_shift.csv \
  --target-path target

This command reads the CSV with dtype=str, runs DataForge detectors, logs any findings with the prefix DATAFORGE_DBT, and enforces the selected mode.

Configuration block

You may add a conservative integration block to profiles.yml:

my_profile:
  target: dev
  outputs:
    dev:
      type: duckdb
      path: ./dev.duckdb
  dataforge:
    mode: dry_run
    target_path: target

Macro arguments remain the source of truth for the common case. The profile block is useful for central defaults in CI; an explicit hook or dispatcher --mode always overrides dataforge.mode from profiles.yml.

Free-tier test setup

The integration tests use DuckDB only. No Snowflake, BigQuery, or paid warehouse account is required.

python -m pip install -e ".[dev]"
python -m pytest

The test project includes a seed CSV with a known decimal-shift anomaly and asserts that DataForge logs the issue.

Publishing

The package is not published yet. The intended release path is PyPI Trusted Publishing after repository ownership is configured; do not add PyPI API tokens to GitHub Secrets.

When this is the wrong tool

Use dbt tests or warehouse constraints first for deterministic rules such as not_null, unique, accepted values, and referential integrity. Use dataforge-dbt when you want DataForge's anomaly detectors and repair audit trail around messy values that are hard to express as static SQL tests.