dbt-unit-testing

name	dbt-unit-testing
description	Proactive skill for creating dbt unit tests. Auto-activates when working with dbt model testing, mock inputs/outputs, or transformation validation. Covers the Model-Inputs-Outputs pattern, format selection (dict/CSV/SQL), BigQuery-specific caveats, and production deployment guidance.

dbt Unit Testing Skill

On Activation

Before proceeding, append a one-line entry to .wire/execution_log.md:

| YYYY-MM-DD HH:MM | skill | dbt-unit-testing | activated | dbt unit testing work triggered this skill |

If .wire/execution_log.md does not exist, create it with the standard header first (see specs/utils/execution_log.md). If no .wire/ directory exists in the current repo, skip this step.

Purpose

This skill automatically activates when creating or discussing dbt unit tests. It guides the creation of effective unit tests using dbt's native unit testing framework (dbt Core 1.8+), ensuring that complex transformation logic is verified with mock inputs and expected outputs before deployment.

Unit tests validate transformation logic in isolation -- they confirm that given specific input rows, a model produces the expected output rows. They complement schema tests (not_null, unique, relationships) which validate data quality in production.

When This Skill Activates

User-Triggered Activation

This skill should activate when users:

• Request unit tests: "Add unit tests for this model"
• Discuss transformation testing: "How do I test this window function logic?"
• Work with mock data: "I need to mock inputs for testing"
• Debug test failures: "My unit test is failing with unexpected output"
• Ask about test coverage: "Which models need unit tests?"

Keywords to watch for:

• "unit test", "test logic", "mock inputs", "test transformation"
• "model-inputs-outputs", "expected output", "test fixtures"
• "dbt test --select test_type:unit"
• "dict format", "csv format", "sql format" (in testing context)

Self-Triggered Activation (Proactive)

Activate BEFORE writing or reviewing unit tests when:

• You're about to create a unit test YAML file
• You detect complex transformation logic that should be unit tested
• User asks to "test" a model that contains business logic
• You're reviewing a model with regex, date calculations, window functions, or case statements
• You encounter a unit_tests: block in any YAML file

Example internal triggers:

• "I'll add tests for this model..." and the model has complex logic -> Activate skill first
• User shows a model with window functions -> Suggest unit tests
• "Let me write a unit test..." -> Check format selection and conventions

When NOT to Activate

• Schema tests (not_null, unique, accepted_values, relationships): Defer to the dbt-development skill
• Test coverage strategy (which tests to write across a project): Refer to testing-reference.md in the dbt-development skill
• Data quality monitoring (freshness, volume checks): Outside scope of unit tests
• Integration/end-to-end testing: Unit tests are for isolated logic validation only

---

Instructions

1. When to Write Unit Tests

Unit tests are valuable when a model contains complex transformation logic that could break silently. Write unit tests for models that include:

Logic Type	Example	Why Unit Test?
Regex extraction	regexp_extract(url, r'/product/(\d+)')	Regex is error-prone and hard to validate visually
Date calculations	date_diff(created_at, closed_at, DAY)	Edge cases around month boundaries, nulls
Window functions	row_number() over (partition by customer_id order by updated_at desc)	Partitioning and ordering logic is subtle
Case statements	Multi-branch CASE WHEN with business rules	Business logic branches need explicit verification
Multi-join logic	Joining 3+ tables with conditional inclusion	Join conditions can silently drop or duplicate rows
Conditional aggregation	sum(case when status = 'active' then amount end)	Filter conditions within aggregations
Type casting with logic	safe_cast with fallback values	Edge cases in type conversion
Deduplication	qualify row_number() over (...) = 1	Dedup key selection affects which row survives
Pivoting/unpivoting	Dynamic column generation	Column mapping is fragile
Currency/unit conversion	Rate application with rounding	Precision and rounding errors

2. When NOT to Write Unit Tests

Do not write unit tests for:

• Simple ref/source pass-through: Models that just select columns from a single source without transformation
• Built-in function wrappers: coalesce(field, 'Unknown'), lower(email), trim(name) -- these are already tested by the database engine
• Pure SQL without business logic: Simple joins that just combine two tables on a foreign key
• Materialization-only models: Models whose purpose is performance (e.g., incremental refresh) rather than logic
• Models with only rename/recast: Staging models that only rename columns and cast types

Rule of thumb: If the model's SQL could be understood correctly by reading it once, it probably does not need a unit test. If you need to "think through" what the SQL does, it needs a unit test.

---

3. The Model-Inputs-Outputs Pattern

Every dbt unit test follows the same pattern:

MODEL (what we're testing)
  + INPUTS (mock data fed to the model's dependencies)
  = EXPECTED OUTPUTS (what the model should produce)

How it works:

1. You specify the model to test
2. You provide mock input rows for each ref() or source() the model depends on
3. You define the expected output rows the model should produce
4. dbt replaces real tables with your mock data, runs the model, and compares actual vs expected output

Key concepts:

• Only mock the inputs relevant to your test case -- you don't need to mock every column
• Columns not included in mocks get default values (NULL for nullable, type-appropriate defaults otherwise)
• Each unit test should test one specific behavior -- not the entire model
• Multiple unit tests per model is encouraged and expected

---

4. Format Selection

dbt supports three formats for specifying mock data. Choose based on readability and requirements:

Dict Format (Default -- Use This First)

Best for: Most cases. Readable, explicit, easy to maintain.

unit_tests:
  - name: test_status_classification
    model: int_orders_classified
    given:
      - input: ref('stg_shopify__orders')
        rows:
          - {order_id: 1, status: "fulfilled", total: 100.00}
          - {order_id: 2, status: "pending", total: 50.00}
          - {order_id: 3, status: "cancelled", total: 75.00}
    expect:
      rows:
        - {order_id: 1, status_category: "complete", total: 100.00}
        - {order_id: 2, status_category: "in_progress", total: 50.00}
        - {order_id: 3, status_category: "cancelled", total: 75.00}

Advantages: Most readable, easy to diff in code review, clear column-value mapping.

Limitations: Can become verbose for many columns.

CSV Format

Best for: Tabular data with many columns or many rows.

unit_tests:
  - name: test_revenue_calculation
    model: int_orders_revenue
    given:
      - input: ref('stg_shopify__orders')
        format: csv
        rows: |
          order_id,quantity,unit_price,discount_pct
          1,10,25.00,0.10
          2,5,50.00,0.00
          3,1,100.00,0.25
    expect:
      format: csv
      rows: |
        order_id,gross_revenue,net_revenue
        1,250.00,225.00
        2,250.00,250.00
        3,100.00,75.00

Advantages: Compact, easy to read as a table, quick to add rows.

Limitations: Harder to spot column alignment issues. String values with commas need quoting.

SQL Format

Best for: Complex data setup, ephemeral model dependencies, or when you need database functions.

unit_tests:
  - name: test_date_spine_join
    model: fct_daily_active_users
    given:
      - input: ref('stg_app__user_sessions')
        format: sql
        rows: |
          SELECT 1 AS user_id, TIMESTAMP '2024-01-15 10:00:00' AS session_start
          UNION ALL
          SELECT 1 AS user_id, TIMESTAMP '2024-01-15 14:00:00' AS session_start
          UNION ALL
          SELECT 2 AS user_id, TIMESTAMP '2024-01-16 09:00:00' AS session_start
    expect:
      format: sql
      rows: |
        SELECT DATE '2024-01-15' AS activity_date, 1 AS active_users
        UNION ALL
        SELECT DATE '2024-01-16' AS activity_date, 1 AS active_users

Advantages: Full SQL expressiveness, can use database functions for dates/timestamps, required for mocking ephemeral model dependencies.

Limitations: Least readable, harder to maintain, database-specific syntax.

When SQL format is required:

• Mocking an ephemeral model dependency (ephemeral models have no table to replace)
• Using database-specific functions to generate test data (e.g., GENERATE_DATE_ARRAY in BigQuery)
• Complex data types that cannot be expressed in dict/CSV format

---

5. Writing the Unit Test YAML

File Location

Unit test definitions go in the model's schema YAML file or in a dedicated test file:

models/
  staging/
    shopify/
      stg_shopify__orders.sql
      stg_shopify__orders.yml        # schema + unit tests here
  integration/
    int_orders_classified.sql
    int_orders_classified.yml        # or here
  warehouse/
    fct_orders.sql
    unit_test_fct_orders.yml         # or in dedicated file (RA convention)

Full Syntax Reference

unit_tests:
  - name: test_<descriptive_name>          # Required. Snake_case, prefix with test_
    description: >                          # Optional but recommended
      Verify that [specific behavior] produces [expected result]
      when given [specific input conditions].
    model: <model_name>                     # Required. The model being tested
    config:
      tags: ["unit-test"]                   # Optional. Useful for selective runs
    given:                                  # Required. List of mock inputs
      - input: ref('<upstream_model>')      # or source('schema', 'table')
        rows:                               # Mock rows for this input
          - {col1: val1, col2: val2}
          - {col1: val3, col2: val4}
      - input: ref('<another_model>')
        rows:
          - {col_a: val_a}
    expect:                                 # Required. Expected output rows
      rows:
        - {result_col1: expected1, result_col2: expected2}
        - {result_col1: expected3, result_col2: expected4}

  - name: test_<another_behavior>           # Multiple tests per model
    model: <model_name>
    given:
      - input: ref('<upstream_model>')
        rows: []                            # Empty input -- tests zero-row handling
    expect:
      rows: []                              # Expect empty output

Overriding Macros and Vars

unit_tests:
  - name: test_with_custom_var
    model: my_model
    overrides:
      vars:
        my_date_var: "2024-01-15"           # Override project vars
      macros:
        my_custom_macro: "hardcoded_value"  # Override macro return values
    given:
      - input: ref('upstream')
        rows:
          - {id: 1}
    expect:
      rows:
        - {id: 1, computed_date: "2024-01-15"}

Testing Incremental Models

For incremental models, you can provide the "existing" state of the model using this:

unit_tests:
  - name: test_incremental_merge
    model: fct_orders
    overrides:
      macros:
        is_incremental: true                # Simulate incremental run
    given:
      - input: this                         # The current state of the model
        rows:
          - {order_id: 1, status: "pending", updated_at: "2024-01-01"}
      - input: ref('stg_orders')            # New incoming data
        rows:
          - {order_id: 1, status: "shipped", updated_at: "2024-01-15"}
          - {order_id: 2, status: "pending", updated_at: "2024-01-15"}
    expect:
      rows:
        - {order_id: 1, status: "shipped", updated_at: "2024-01-15"}
        - {order_id: 2, status: "pending", updated_at: "2024-01-15"}

---

6. Running Unit Tests

Execute Unit Tests

# Run all unit tests in the project
dbt test --select test_type:unit

# Run unit tests for a specific model
dbt test --select test_type:unit,model_name

# Run a specific unit test by name
dbt test --select test_name

# Run unit tests as part of a full build
dbt build --select +my_model   # Runs model + all associated tests

# Run with verbose output for debugging
dbt test --select test_type:unit -v

The --empty Flag

Use --empty to validate that unit test YAML parses correctly without actually running against the database:

dbt test --select test_type:unit --empty

This is useful for CI validation of test syntax before merging.

---

7. Interpreting Failures

When a unit test fails, dbt shows a diff between expected and actual output:

FAIL 1/1 test_status_classification

Failure in unit_test test_status_classification (models/integration/int_orders_classified.yml)

Got 1 result, configured to fail if != 0

  actual                        | expected                      | diff
  {order_id: 1,                 | {order_id: 1,                 |
   status_category: "active"}   |  status_category: "complete"} | !=

How to read the diff:

• actual: What the model actually produced
• expected: What you said it should produce
• diff: != indicates mismatched rows

Common failure causes:

1. Logic error in the model -- The model's SQL is wrong. Fix the SQL.
2. Wrong expected value -- Your expectation was incorrect. Fix the test.
3. Missing mock input -- A dependency wasn't mocked, causing NULLs. Add the missing input.
4. Row ordering -- Unit tests compare sets, not ordered lists. Order should not matter, but if it does, check for non-deterministic ordering in the model.
5. Type mismatch -- Expected "100" (string) but got 100 (integer). Match types exactly.
6. Precision mismatch -- Expected 100.00 but got 100.0 or 99.999999. See BigQuery caveats below.

---

8. BigQuery-Specific Caveats

When writing unit tests for BigQuery targets, be aware of these platform-specific behaviors:

STRUCT Handling

BigQuery STRUCT types require special handling in unit tests. Use SQL format for STRUCT inputs:

unit_tests:
  - name: test_struct_extraction
    model: stg_ga4__events
    given:
      - input: source('google_analytics', 'events')
        format: sql
        rows: |
          SELECT
            'event_1' AS event_id,
            STRUCT('page_view' AS value) AS event_name,
            [STRUCT('page_location' AS key, STRUCT('https://example.com' AS string_value) AS value)] AS event_params
    expect:
      rows:
        - {event_id: "event_1", event_name: "page_view", page_location: "https://example.com"}

TIMESTAMP Precision

BigQuery TIMESTAMPs have microsecond precision. When comparing timestamps in expected output, match the precision:

# Correct -- explicit precision
expect:
  rows:
    - {created_at: "2024-01-15 10:00:00.000000 UTC"}

# Risky -- may fail depending on how the model outputs timestamps
expect:
  rows:
    - {created_at: "2024-01-15 10:00:00"}

Use SQL format if timestamp precision is problematic:

expect:
  format: sql
  rows: |
    SELECT TIMESTAMP '2024-01-15 10:00:00 UTC' AS created_at

GEOGRAPHY Type

BigQuery GEOGRAPHY columns cannot be compared directly in dict format. Use SQL format with ST_GEOGPOINT:

given:
  - input: ref('stg_locations')
    format: sql
    rows: |
      SELECT 1 AS location_id, ST_GEOGPOINT(-122.4194, 37.7749) AS geo_point

NUMERIC/BIGNUMERIC Precision

BigQuery NUMERIC (38 digits, 9 decimal) and BIGNUMERIC (76 digits, 38 decimal) may cause precision mismatches:

# Use explicit cast in SQL format for precision-sensitive comparisons
expect:
  format: sql
  rows: |
    SELECT NUMERIC '123.456789' AS amount

DATE and DATETIME Formatting

BigQuery distinguishes between DATE, DATETIME, and TIMESTAMP. Ensure your mock data matches the column type:

# DATE column
rows:
  - {order_date: "2024-01-15"}

# DATETIME column
rows:
  - {created_datetime: "2024-01-15T10:00:00"}

# TIMESTAMP column (requires timezone)
rows:
  - {created_at: "2024-01-15 10:00:00 UTC"}

Snowflake-Specific Notes

For projects targeting Snowflake:

• VARIANT columns: Use SQL format with PARSE_JSON()
• TIMESTAMP_NTZ vs TIMESTAMP_TZ: Be explicit about timezone handling
• ARRAY columns: Use SQL format with ARRAY_CONSTRUCT()

---

9. Production Deployment

Unit tests should run in CI/CD (development and staging) but typically not in production scheduled jobs:

# Production job -- exclude unit tests
dbt build --exclude-resource-type unit_test

# CI/CD job -- include unit tests
dbt build --select state:modified+
# (unit tests run automatically when their model is selected)

Rationale: Unit tests validate logic correctness, which is a development concern. Production jobs should run schema tests (data quality) but not re-validate logic that was already verified in CI.

dbt Cloud configuration:

• In CI jobs: Unit tests run by default when a model is selected
• In Production jobs: Add --exclude-resource-type unit_test to the job command
• In Slim CI: Unit tests for modified models run automatically with state:modified+

---

10. Common Mistakes

Mistake	Problem	Fix
Testing too much in one test	One test validates 5 different behaviors	Split into separate focused tests
Mocking every column	Test is brittle and hard to maintain	Mock only columns relevant to the behavior under test
Not mocking a dependency	NULL values propagate, test fails mysteriously	Ensure every ref() and source() used by the tested code path is mocked
Wrong data types in mock	"100" (string) vs 100 (integer)	Match column types from the source schema
Testing implementation, not behavior	Test breaks when SQL is refactored	Test the output contract, not how the SQL achieves it
Ignoring NULL handling	Model works with mock data but fails on real NULLs	Include NULL values in mock inputs to test edge cases
Copy-pasting between tests	Tests drift out of sync, hard to maintain	Use YAML anchors or extract common fixtures
Not testing empty inputs	Model crashes on zero rows in production	Add a test with rows: [] for each input
Forgetting format: sql for ephemeral deps	Test fails because ephemeral model has no table	Always use SQL format when mocking ephemeral model dependencies
Hardcoding dates that expire	Test breaks when "tomorrow" becomes "yesterday"	Use overrides.vars with a fixed reference date

---

RA-Specific Conventions

Unit Test Requirements by Layer

Model Layer	Unit Test Required?	Guidance
Staging (stg_)	Recommended if non-trivial	Only if the staging model contains regex, date logic, or case statements beyond simple rename/recast
Integration (int_)	Required if business logic present	Multi-source joins with conditional logic, entity resolution, deduplication
Warehouse - Dimension (_dim)	Required	SCD logic, derived attributes, business classifications
Warehouse - Fact (_fact)	Required	Metric calculations, status derivations, temporal logic

File Naming Convention

Use dedicated unit test files with the naming pattern:

unit_test_{model_name}.yml

Place in the same directory as the model's schema YAML:

models/
  warehouse/
    fct_orders.sql
    fct_orders.yml                 # Schema tests, column descriptions
    unit_test_fct_orders.yml       # Unit tests (separate file)

Rationale: Separating unit tests from schema definitions keeps both files manageable and makes it easy to find all unit tests via glob patterns (unit_test_*.yml).

Test Naming Convention

unit_tests:
  - name: test_{model_name}__{behavior_description}
    # Examples:
    # test_fct_orders__cancelled_orders_excluded_from_revenue
    # test_int_customers__dedup_keeps_latest_record
    # test_dim_products__inactive_products_flagged

Cross-Reference: Overall Test Strategy

For guidance on which types of tests to write (schema tests, unit tests, data tests) and how much coverage is needed per model layer, refer to testing-reference.md in the dbt-development skill. This skill covers only the mechanics of writing unit tests.

---

Additional Resources

• Examples: See the examples/ directory for complete, runnable unit test files
  • unit-test-example.yml -- Simple staging model unit test
  • unit-test-complex.yml -- Complex unit test with window functions and date logic
• dbt Documentation: Unit Tests (dbt Core 1.8+)

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Attribution

Adapted from dbt-labs/dbt-agent-skills (Apache-2.0 License). Original skill: adding-dbt-unit-test. Modified for Rittman Analytics conventions, BigQuery focus, and Wire Framework integration.