Testing Data Quality

Ensuring data quality is critical for any analytics pipeline. dbt provides built-in testing capabilities that help catch issues early, enforce data integrity, and maintain confidence in your transformations. This guide explains how to implement tests in your dbt project.

Why Testing Matters in dbt

Data tests serve several essential purposes:

Validate assumptions about your data
Catch errors before they impact downstream consumers
Document expectations about data properties
Ensure consistency across transformations

Without testing, issues can creep into your data pipeline, potentially leading to incorrect business decisions or loss of trust in your analytics.

Benefits of dbt Testing

Ensures Data Integrity – Prevents duplicates, null values, and referential mismatches.
Validates Business Logic – Confirms that data meets expected criteria.
Catches Issues Early – Detects errors before they affect downstream analytics.
Automates Quality Checks – Reduces the need for manual data validation.
Supports Collaboration – Helps teams align on data expectations.

Types of dbt Tests

dbt supports two main types of tests:

1. Generic Tests (Built-in)

Generic tests are reusable test definitions that can be applied to multiple models and columns. dbt includes four built-in generic tests:

Test	Purpose	Example Use
`unique`	Ensures a column has no duplicate values	Primary keys, email addresses
`not_null`	Ensures a column contains no NULL values	Required fields, join keys
`accepted_values`	Validates that column values are within a specified list	Status fields, categories
`relationships`	Ensures referential integrity between tables	Foreign keys, dimensional references

2. Singular Tests

Singular tests are custom SQL queries that define specific test logic. These are one-off tests written as SQL queries that return failing records.

Adding Tests to Your Models

Tests in dbt are typically defined in YAML files alongside your models.

Generic Tests Example

# models/schema.yml
version: 2

models:
  - name: customers
    columns:
      - name: customer_id
        tests:
          - unique
          - not_null
      - name: email
        tests:
          - unique
          - not_null
      - name: status
        tests:
          - accepted_values:
              values: ['active', 'inactive', 'pending']
      - name: country_id
        tests:
          - relationships:
              to: ref('countries')
              field: id

This YAML configuration:

Tests that customer_id values are unique and not null
Tests that email values are unique and not null
Tests that status values are only ‘active’, ‘inactive’, or ‘pending’
Tests that each country_id exists in the countries table

Singular Test Example

Singular tests are SQL files in the tests/ directory:

-- tests/assert_total_payment_amount_matches_order_amount.sql
-- This test checks that payment amounts sum to order amounts
SELECT
  orders.order_id,
  orders.amount as order_amount,
  SUM(payments.amount) as payment_amount
FROM {{ ref('orders') }}
LEFT JOIN {{ ref('payments') }} ON orders.order_id = payments.order_id
GROUP BY orders.order_id, orders.amount
HAVING ABS(orders.amount - SUM(COALESCE(payments.amount, 0))) > 0.01

This test identifies orders where the payment amounts don’t match the order amount.

Running Tests

dbt makes it easy to run tests as part of your workflow.

Operation	Command	Description
Running All Tests	`dbt test`	Runs all tests in your dbt project
Testing Specific Models	`dbt test --models customers`	Runs all tests associated with a specific model
Running a Single Test	`dbt test --select test_name`	Runs a specific test by name
Testing Critical Models Only	`dbt test --select tag:critical`	Runs tests only for models tagged as ‘critical’

Test Configuration Options

You can configure how tests behave using additional parameters.

Setting Severity Levels

Tests can be warnings instead of errors:

models:
  - name: orders
    columns:
      - name: status
        tests:
          - accepted_values:
              values: ['completed', 'shipped', 'returned']
              severity: warn  # Won't cause pipelines to fail

Store Test Failures

Save test failures for analysis:

models:
  - name: large_table
    columns:
      - name: id
        tests:
          - unique:
              config:
                store_failures: true  # Saves failures to a table

Limiting Failure Volume

Control how many failures are reported:

models:
  - name: events
    columns:
      - name: event_id
        tests:
          - unique:
              config:
                limit: 100  # Only show first 100 failures

Creating Custom Generic Tests

You can extend dbt’s testing capabilities by creating custom generic tests as macros:

-- macros/test_is_valid_email.sql
{% macro test_is_valid_email(model, column_name) %}

select *
from {{ model }}
where
    {{ column_name }} is not null
    and not regexp_like(
        {{ column_name }},
        '^[a-zA-Z0-9._%+-]+@[a-zA-Z0-9.-]+\\.[a-zA-Z]{2,}$'
    )

{% endmacro %}

Then use it just like built-in tests:

models:
  - name: customers
    columns:
      - name: email
        tests:
          - is_valid_email

Test Organization Strategies

As your project grows, organizing tests becomes important:

Test by Business Domain

Group tests alongside the models they validate:

models/
├── marketing/
│   ├── schema.yml      # Contains tests for marketing models
│   ├── campaigns.sql
│   └── ad_performance.sql
└── finance/
    ├── schema.yml      # Contains tests for finance models
    ├── transactions.sql
    └── accounts.sql

Centralized Tests

Maintain all tests in a dedicated location:

models/
└── ...
tests/
├── generic/            # Custom generic tests
│   └── is_valid_email.sql
└── singular/           # Singular tests
    ├── marketing/
    │   └── campaign_consistency.sql
    └── finance/
        └── transaction_reconciliation.sql

Troubleshooting Failed Tests

When tests fail, dbt provides information to help diagnose the issue:

Review test SQL: dbt outputs the SQL for the failing test
Examine failing records: Look at examples of failing records
Check compiled SQL: Review the compiled test SQL in target/compiled/
Store failures: Use store_failures: true to analyze failure patterns

Test Output Examples

Passing Tests

42:10 | 1 of 4 START test not_null_orders_order_id ............................ [RUN]
42:10 | 1 of 4 PASS not_null_orders_order_id ................................. [PASS in 0.08s]
42:10 | 2 of 4 START test unique_orders_order_id ............................. [RUN]
42:10 | 2 of 4 PASS unique_orders_order_id ................................... [PASS in 0.10s]

Failing Tests

42:11 | 3 of 4 START test accepted_values_orders_status_completed__shipped__returned ... [RUN]
42:11 | 3 of 4 FAIL accepted_values_orders_status_completed__shipped__returned ... [FAIL in 0.15s]
42:11 | 4 of 4 START test relationships_orders_customer_id__customer_id__ref_customers_ ... [RUN]
42:11 | 4 of 4 FAIL relationships_orders_customer_id__customer_id__ref_customers_ ... [FAIL in 0.19s]

For failing tests, dbt shows details about the failures:

Failure in test relationships_orders_customer_id__customer_id__ref_customers_
Got 2 results, expected 0.

compiled SQL at target/compiled/.../relationships_orders_customer_id__customer_id__ref_customers_.sql

Best Practices for dbt Testing

Test Coverage Strategy

Test primary keys with unique and not_null
Test foreign keys with relationships
Test categorical fields with accepted_values
Test business logic with singular tests
Focus on testing critical data first

Test Organization

Use a consistent naming convention for singular tests
Group related tests together
Document what each test validates

Test Execution

Run tests before finalizing model changes
Include tests in CI/CD pipelines
Alert on test failures in production

Test Maintainability

Prefer generic tests for common validations
Create custom generic tests for repeated patterns
Use macros to generate complex test logic

By implementing a robust testing strategy in dbt, you can ensure your data transformations maintain high quality and reliability, building trust in your analytics data among stakeholders.

​Why Testing Matters in dbt

​Types of dbt Tests

​1. Generic Tests (Built-in)

​2. Singular Tests

​Adding Tests to Your Models

​Generic Tests Example

​Singular Test Example

​Running Tests

​Test Configuration Options

​Setting Severity Levels

​Store Test Failures

​Limiting Failure Volume

​Creating Custom Generic Tests

​Test Organization Strategies

​Test by Business Domain

​Centralized Tests

​Troubleshooting Failed Tests

​Test Output Examples

​Passing Tests

​Failing Tests

​Best Practices for dbt Testing