Coalesce Quality options for dbt

Customize how dbt resources appear in Coalesce Quality using meta configuration

This guide explains how to use dbt’s meta configuration to customize how your dbt resources are displayed and categorized in Coalesce Quality.

Prerequisites:

• A working dbt Core or dbt Cloud integration with Coalesce Quality
• Familiarity with dbt’s meta configuration

Overview

Coalesce Quality reads the quality key of a dbt test’s meta configuration. Use it to set the check categories of a test:

• check_category sets the technical category, for example nullness or e2e.
• governance_category sets the governance category, for example completeness or timeliness.

Check category

Use the check_category option to assign a dbt test to a technical check category. This is useful for singular tests and custom generic tests, which often have no category of their own.

Configuration

Add the quality configuration to your test’s meta block:

{{ config(
    meta = {
        'quality': {'check_category': 'e2e'}
    }
)
}}

The original synq namespace remains supported permanently, so existing meta: {synq: {check_category: ...}} configurations do not need to change. Use quality for new configurations.

Governance category

Use governance_category in the same way to set the governance category of a test. You can set both keys on one test:

models:
  - name: orders
    columns:
      - name: order_id
        tests:
          - not_null:
              meta:
                quality:
                  check_category: 'nullness'
                  governance_category: 'completeness'

Example: Singular test

For a singular test file (e.g., tests/e2e_order_flow.sql):

{{ config(
    meta = {
        'quality': {'check_category': 'e2e'}
    }
)
}}

-- Test that verifies the complete order flow from creation to fulfillment
SELECT order_id
FROM {{ ref('orders') }}
WHERE status = 'created'
  AND created_at < CURRENT_TIMESTAMP - INTERVAL '24 hours'
  AND fulfilled_at IS NULL

Example: Generic test in schema.yml

For generic tests defined in your schema files:

models:
  - name: orders
    columns:
      - name: order_id
        tests:
          - unique:
              meta:
                quality:
                  check_category: 'data-quality'
          - not_null:
              meta:
                quality:
                  check_category: 'data-quality'

Example: Custom generic test

For custom generic tests, add the config at the top of the test file:

{% test custom_validation(model, column_name) %}

{{ config(
    meta = {
        'quality': {'check_category': 'validation'}
    }
)
}}

SELECT {{ column_name }}
FROM {{ model }}
WHERE {{ column_name }} IS NOT NULL
  AND NOT {{ column_name }} REGEXP '^[A-Z]{2}[0-9]{6}$'

{% endtest %}

How it works

Coalesce Quality categorizes dbt tests automatically from the test type and package (e.g., dbt, dbt_expectations, dbt_utils). For singular tests and custom generic tests it often cannot find a category.

A category set in meta replaces the automatic category of that test. Only a per-check override set by a workspace admin takes priority over it. See Where categories come from.

Custom categories

You can use any category name that makes sense for your organization. Common examples:

Category	Description
e2e	End-to-end tests that validate complete workflows
data-quality	Tests for data completeness, accuracy, and consistency
business-rules	Tests that enforce business logic and constraints
reconciliation	Tests that compare data across sources
validation	Input validation and format checks
sla	Service level agreement compliance tests

Default categories

Coalesce Quality assigns technical categories to known test types from dbt, dbt_expectations, dbt_utils and other packages. The most common ones are:

Category	Description
accepted_values	Tests that validate values are within an allowed set
nullness	Tests for null or not-null constraints
unique	Uniqueness validation tests
relationships	Referential integrity and foreign key tests
freshness	Data timeliness and recency tests
row_count	Row count validation and volume tests
range_validation	Tests that check values are within expected ranges
comparison	Tests comparing values between columns or tables
structure	Schema structure and column existence tests
match_regex	Pattern matching and regex validation tests

Use the same category names across your project, and write down what each custom category means so that your team uses them the same way.