dbt-testing

name	dbt-testing
description	dbt testing strategies using dbt_constraints for database-level enforcement, generic tests, and singular tests. Use this skill when implementing data quality checks, adding primary/foreign key constraints, creating custom tests, or establishing comprehensive testing frameworks across bronze/silver/gold layers.

dbt Testing

Purpose

Transform AI agents into experts on dbt testing strategies, providing guidance on implementing comprehensive data quality checks with database-enforced constraints, generic tests, and custom singular tests to ensure data integrity across all layers.

When to Use This Skill

Activate this skill when users ask about:

• Implementing data quality tests
• Adding primary key and foreign key constraints
• Using dbt_constraints package for database-level enforcement
• Creating generic (reusable) tests
• Writing singular (one-off) tests
• Testing strategies by layer (bronze/silver/gold)
• Debugging test failures
• Configuring test severity levels
• Storing test failures for analysis

Official dbt Documentation: Testing

---

Testing Philosophy

Implement tests in this order for maximum data quality:

1. Primary Keys - Every dimension must have one
2. Foreign Keys - All fact relationships
3. Unique Keys - Business key constraints
4. Business Rules - Domain-specific validations
5. Data Quality - Completeness, accuracy, consistency

---

Why Use dbt_constraints?

The dbt_constraints package provides database-level enforcement (not just dbt tests):

✅ Database Enforcement - Creates actual constraints in the data warehouse ✅ Performance - Database-level constraints improve query optimization ✅ Data Integrity - Prevents invalid data at all access points (not just dbt) ✅ Documentation - Constraints visible in database metadata and BI tools ✅ Query Optimization - Database can use constraints for better execution plans

Standard dbt tests only validate during dbt test runs. dbt_constraints creates real database constraints that are enforced 24/7.

Official dbt_constraints Documentation: GitHub - Snowflake-Labs/dbt_constraints

---

Package Installation

# packages.yml
packages:
  - package: Snowflake-Labs/dbt_constraints
    version: [">=0.8.0", "<1.0.0"]

  - package: dbt-labs/dbt_utils
    version: [">=1.0.0", "<2.0.0"]

Install packages:

dbt deps

Official dbt Docs: Package Management

---

Primary Key Testing

Simple Primary Key (dbt_constraints)

Required for every dimension:

# models/gold/_models.yml
models:
  - name: dim_customers
    columns:
      - name: customer_id
        tests:
          - dbt_constraints.primary_key

Composite Primary Key

When primary key spans multiple columns:

models:
  - name: fct_order_lines
    tests:
      - dbt_constraints.primary_key:
          column_names:
            - order_id
            - line_number

Alternative: Built-in dbt Tests

Not recommended - no database enforcement:

columns:
  - name: product_id
    tests:
      - not_null
      - unique

Limitation: Only validates during dbt test runs, doesn't prevent bad data from other sources.

---

Foreign Key Testing

Simple Foreign Key (dbt_constraints)

Ensures referential integrity:

models:
  - name: fct_orders
    columns:
      - name: customer_id
        tests:
          - dbt_constraints.foreign_key:
              pk_table_name: ref('dim_customers')
              pk_column_name: customer_id

Multiple Foreign Keys

For facts with multiple dimension relationships:

models:
  - name: fct_order_lines
    columns:
      - name: order_id
        tests:
          - dbt_constraints.foreign_key:
              pk_table_name: ref('fct_orders')
              pk_column_name: order_id

      - name: product_id
        tests:
          - dbt_constraints.foreign_key:
              pk_table_name: ref('dim_products')
              pk_column_name: product_id

      - name: customer_id
        tests:
          - dbt_constraints.foreign_key:
              pk_table_name: ref('dim_customers')
              pk_column_name: customer_id

Alternative: Built-in dbt Relationships Test

Not recommended - no database enforcement:

columns:
  - name: customer_id
    tests:
      - relationships:
          to: ref('dim_customers')
          field: customer_id

---

Unique Key Testing

Simple Unique Key (dbt_constraints)

For business keys (non-primary keys that must be unique):

columns:
  - name: customer_email
    tests:
      - dbt_constraints.unique_key

Composite Unique Key

When uniqueness spans multiple columns:

models:
  - name: stg_orders
    tests:
      - dbt_constraints.unique_key:
          column_names:
            - order_number
            - order_source

---

Generic Tests (Reusable)

Built-in dbt Tests

columns:
  - name: order_status
    tests:
      - not_null
      - accepted_values:
          values: ["pending", "processing", "shipped", "delivered", "cancelled"]

  - name: order_amount
    tests:
      - not_null

dbt_utils Tests

Powerful generic tests from dbt_utils package:

columns:
  - name: customer_email
    tests:
      - dbt_utils.not_null_proportion:
          at_least: 0.95 # 95% of rows must have email

  - name: order_amount
    tests:
      - dbt_utils.accepted_range:
          min_value: 0
          max_value: 1000000

  - name: customer_status
    tests:
      - dbt_utils.not_empty_string

Official dbt_utils Documentation: dbt_utils - Generic Tests

---

Custom Generic Tests

Create reusable test for common patterns:

-- tests/generic/test_positive_values.sql
{% test positive_values(model, column_name) %}

select count(*)
from {{ model }}
where {{ column_name }} <= 0

{% endtest %}

Usage:

columns:
  - name: order_total
    tests:
      - positive_values

  - name: quantity
    tests:
      - positive_values

Another Example: Date Range Test

-- tests/generic/test_recent_data.sql
{% test recent_data(model, column_name, days_ago=30) %}

select count(*)
from {{ model }}
where {{ column_name }} < dateadd(day, -{{ days_ago }}, current_date())

{% endtest %}

Usage:

columns:
  - name: order_date
    tests:
      - recent_data:
          days_ago: 7 # Alert if no orders in last 7 days

---

Singular Tests (One-Off)

For complex business logic that doesn't fit generic tests:

-- tests/singular/test_order_dates_sequential.sql
with date_validation as (
    select
        o.order_id,
        o.order_date,
        c.signup_date
    from {{ ref('fct_orders') }} o
    join {{ ref('dim_customers') }} c
        on o.customer_id = c.customer_id
    where o.order_date < c.signup_date  -- Order before signup = invalid
)

select * from date_validation

Test fails if ANY rows are returned.

More Singular Test Examples

Revenue Reconciliation:

-- tests/singular/test_revenue_reconciliation.sql
-- Ensure fact table revenue matches source system
with fact_revenue as (
    select sum(order_amount) as total_revenue
    from {{ ref('fct_orders') }}
    where order_date = current_date() - 1
),

source_revenue as (
    select sum(amount) as total_revenue
    from {{ source('erp', 'orders') }}
    where order_date = current_date() - 1
),

comparison as (
    select
        f.total_revenue as fact_revenue,
        s.total_revenue as source_revenue,
        abs(f.total_revenue - s.total_revenue) as difference
    from fact_revenue f
    cross join source_revenue s
)

select * from comparison
where difference > 0.01  -- Tolerance of 1 cent

Referential Integrity Check:

-- tests/singular/test_orphaned_orders.sql
-- Find orders with invalid customer_id (not in dim_customers)
select
    o.order_id,
    o.customer_id
from {{ ref('fct_orders') }} o
left join {{ ref('dim_customers') }} c
    on o.customer_id = c.customer_id
where c.customer_id is null
  and o.customer_id != -1  -- Exclude ghost key

Official dbt Documentation: Singular Tests

---

Testing by Layer

Bronze Layer (Staging)

Focus: Basic data quality at source

models:
  - name: stg_tpc_h__customers
    columns:
      - name: customer_id
        tests:
          - dbt_constraints.primary_key

      - name: customer_email
        tests:
          - not_null

Keep it simple - just verify source data integrity.

---

Silver Layer (Intermediate)

Focus: Business rule validation, calculated fields

models:
  - name: int_customers__with_orders
    columns:
      - name: customer_id
        tests:
          - dbt_constraints.primary_key

      - name: lifetime_orders
        tests:
          - not_null
          - dbt_utils.accepted_range:
              min_value: 0

      - name: lifetime_value
        tests:
          - not_null
          - dbt_utils.accepted_range:
              min_value: 0

Add business logic validation - ensure calculated fields make sense.

---

Gold Layer (Marts)

Focus: Comprehensive constraint enforcement with dbt_constraints

models:
  - name: dim_customers
    description: "Customer dimension with full history and metrics"
    columns:
      - name: customer_id
        description: "Unique customer identifier"
        tests:
          - dbt_constraints.primary_key

      - name: customer_tier
        description: "Customer value classification"
        tests:
          - accepted_values:
              values: ["bronze", "silver", "gold", "platinum"]

      - name: customer_email
        tests:
          - dbt_constraints.unique_key

  - name: fct_orders
    description: "Order transactions fact table"
    columns:
      - name: order_id
        tests:
          - dbt_constraints.primary_key

      - name: customer_id
        tests:
          - dbt_constraints.foreign_key:
              pk_table_name: ref('dim_customers')
              pk_column_name: customer_id

      - name: product_id
        tests:
          - dbt_constraints.foreign_key:
              pk_table_name: ref('dim_products')
              pk_column_name: product_id

      - name: order_amount
        tests:
          - not_null
          - dbt_utils.accepted_range:
              min_value: 0

Maximum enforcement - use all constraint types to ensure production data quality.

---

Test Configuration

Store Test Failures

Analyze failed test records:

dbt test --store-failures

# dbt_project.yml
tests:
  +store_failures: true
  +schema: dbt_test_failures

Query failures:

select * from dbt_test_failures.not_null_dim_customers_customer_email

---

Test Severity Levels

Warn vs Error:

columns:
  - name: customer_email
    tests:
      - dbt_constraints.unique_key:
          config:
            severity: warn # or 'error' (default)

Severity Behavior:

• error: Test failure stops dbt execution (exit code 1)
• warn: Test failure logs warning but continues (exit code 0)

Use warn for:

• Data quality checks that shouldn't block deployment
• Known edge cases during migration
• Monitoring tests

---

Limit Test Execution

Test specific model:

dbt test --select dim_customers

Test by type:

dbt test --select test_type:generic    # All generic tests
dbt test --select test_type:singular   # All singular tests

Test with dependencies:

dbt test --select +dim_customers+  # Test model and all dependencies

Official dbt Documentation: Test Selection

---

Running Tests

# Run all tests
dbt test

# Build models and test together (recommended)
dbt build  # Runs models, then tests

# Test specific model
dbt test --select dim_customers

# Test specific column
dbt test --select dim_customers,column:customer_id

# Test by layer
dbt test --select tag:gold

# Test with failures stored
dbt test --store-failures --select fct_orders

Best Practice: Use dbt build instead of dbt run + dbt test separately.

---

Testing Best Practices

1. Test Early and Often

Add tests as you build models, not after deployment.

2. Layer-Appropriate Testing

• Bronze: Basic not_null and primary key tests
• Silver: Business rule validation, range checks
• Gold: Comprehensive constraint enforcement with dbt_constraints

3. Use dbt_constraints for Production

Database-level constraints provide:

• 24/7 enforcement (not just during dbt runs)
• Performance optimization
• Better integration with BI tools

4. Document Test Purpose

columns:
  - name: customer_tier
    description: "Customer segmentation based on lifetime value"
    tests:
      - accepted_values:
          values: ["bronze", "silver", "gold", "platinum"]
          config:
            severity: error

5. Balance Coverage vs Performance

• Don't over-test trivial columns
• Focus on business-critical fields
• Use sampling for very large tables if needed

---

Testing Checklist

Before moving to production:

• All dimensions have primary key tests
• All facts have foreign key tests to dimensions
• Business rules are validated with tests
• Data quality tests are in place (not_null, accepted_values)
• Tests run successfully in CI/CD pipeline
• dbt_constraints enabled for all production marts
• Test failures configured to store in database
• Singular tests created for complex business logic

---

Helping Users with Testing

Strategy for Assisting Users

When users ask about testing:

1. Identify model type: Dimension? Fact? Intermediate?
2. Recommend appropriate tests: By layer and purpose
3. Prioritize constraints: Primary keys → Foreign keys → Business rules
4. Provide complete examples: Working YAML configurations
5. Explain benefits: Why dbt_constraints over standard tests
6. Show how to run: Commands and debugging approaches

Common User Questions

"What tests should I add?"

• Start with dbt_constraints for primary/foreign keys
• Add not_null for required fields
• Use accepted_values for enums
• Create singular tests for complex business logic

"Why use dbt_constraints instead of regular tests?"

• Database-level enforcement (24/7, not just during dbt runs)
• Better query performance
• Prevents bad data from any source
• Visible in database metadata

"How do I debug test failures?"

• Use --store-failures to save failing records
• Query the test failure table
• Review actual data that failed the test
• Add more specific tests to isolate issue

---

Related Official Documentation

• dbt Docs: Tests
• dbt_constraints Package
• dbt_utils Generic Tests
• dbt Docs: Test Selection

---

Goal: Transform AI agents into expert dbt testers who implement comprehensive, database-enforced data quality checks that protect data integrity across all layers and access patterns.