Custom singular tests in dbt - Mini Project: Build & Apply

Creating Custom Singular Tests in dbt

📖 Scenario: You are working on a data project using dbt to ensure your data quality. You want to create a custom singular test to check that no user has a negative age in your users table.

🎯 Goal: Build a custom singular test in dbt that checks for any negative values in the age column of the users table.

📋 What You'll Learn

Create a SQL file for the custom singular test with the exact name no_negative_age.sql.

Define a SQL query that selects rows where age < 0 from the users table.

Add a test configuration variable called model_name with the value users.

Use the model_name variable inside the SQL query to refer to the table dynamically.

Print the final SQL query string to verify the test logic.

💡 Why This Matters

🌍 Real World

Data teams use custom singular tests in dbt to ensure data quality by writing specific checks tailored to their data models.

💼 Career

Knowing how to write custom tests in dbt is valuable for data analysts and engineers to maintain trustworthy data pipelines.

1

Create the custom singular test SQL file

Create a file named no_negative_age.sql and write a SQL query that selects all rows from the users table where the age column is less than 0. Use the exact table name users in the query.

dbt

-- Write a SQL query to select rows where age is less than 0 from users table
-- Your code here

Hint

Write a simple SQL SELECT statement filtering age < 0 from users.

2

Add a configuration variable for the model name

Add a variable called model_name and set it to the string users. This variable will be used to refer to the table dynamically in the test.

dbt

model_name = 'users'

-- Your code here

Hint

Define a Python variable model_name with value 'users'.

3

Use the variable inside the SQL query

Modify the SQL query to use the model_name variable instead of the hardcoded table name. Use an f-string to insert model_name into the query string.

dbt

model_name = 'users'

query = f"select * from {model_name} where age < 0"
# Your code here

Hint

Use an f-string to build the query with model_name inside curly braces.

4

Print the final SQL query

Print the variable query to display the final SQL query string that will be used in the custom singular test.

dbt

model_name = 'users'

query = f"select * from {model_name} where age < 0"

# Print the query
# Your code here

Hint

Use print(query) to show the SQL query string.

Practice

1. What is the main purpose of a custom singular test in dbt?

easy

A. To automatically generate documentation for your models

B. To write your own SQL query that checks data quality and returns rows only if there are issues

C. To schedule dbt runs at specific times

D. To create new tables from existing data

Solution

1. Step 1: Understand the role of custom singular tests

   Custom singular tests are SQL queries that check data quality by returning rows only when problems exist.

2. Step 2: Compare options with this definition

   Only To write your own SQL query that checks data quality and returns rows only if there are issues describes writing a SQL query that returns rows if there are data issues, matching the purpose of custom singular tests.

3. Final Answer:

   To write your own SQL query that checks data quality and returns rows only if there are issues -> Option B

4. Quick Check:

   Custom singular test = SQL check returning problem rows [OK]

Hint: Custom singular tests return rows only when data has problems [OK]

Common Mistakes:

• Confusing tests with documentation generation
• Thinking tests create tables
• Assuming tests schedule runs

2. Which of the following is the correct way to define a custom singular test in your schema.yml file?

easy

A. tests:
- my_custom_test.sql

B. tests:
- my_custom_test:
sql: my_custom_test.sql

C. tests:
- name: my_custom_test
test: my_custom_test

D. tests:
- my_custom_test

Solution

1. Step 1: Recall the schema.yml syntax for custom singular tests

   Custom singular tests are referenced by their filename (without .sql) in the tests list of schema.yml.

2. Step 2: Match options to this syntax

   tests: - my_custom_test correctly references the test file tests/my_custom_test.sql. Other options use incorrect structure, extra keys, or include .sql.

3. Final Answer:

   tests: - my_custom_test -> Option D

4. Quick Check:

   schema.yml test syntax = - test_filename_without_sql [OK]

Hint: Reference tests by name (no .sql) in tests: list [OK]

Common Mistakes:

• Using 'name' or 'test' keys
• Including .sql extension
• Using map/dict structure

3. Given the following custom singular test SQL in tests/check_positive_values.sql:

SELECT * FROM {{ ref('orders') }} WHERE amount <= 0

What will be the output if all amounts in the orders table are positive?

medium

A. An empty result with zero rows

B. A table with all rows where amount is less than or equal to zero

C. An error because of invalid SQL syntax

D. A count of rows with amount less than or equal to zero

Solution

1. Step 1: Understand the test SQL logic

   The test selects rows where amount is less than or equal to zero.

2. Step 2: Analyze the data condition

   If all amounts are positive, no rows satisfy the condition, so the query returns zero rows.

3. Final Answer:

   An empty result with zero rows -> Option A

4. Quick Check:

   All positive amounts means zero rows returned [OK]

Hint: No matching rows means test passes with empty output [OK]

Common Mistakes:

• Expecting a count instead of rows
• Thinking it returns all rows
• Assuming SQL syntax error

4. You wrote a custom singular test SQL file but when running dbt test, it fails with a syntax error. Which of the following is the most likely cause?

medium

A. The model referenced in {{ ref() }} does not exist

B. The test SQL returns zero rows

C. The SQL file is missing the required SELECT statement

D. The test is not listed in schema.yml

Solution

1. Step 1: Identify causes of SQL syntax errors

   Syntax errors happen when SQL is malformed, such as missing SELECT statements.

2. Step 2: Evaluate options for syntax error causes

   The SQL file is missing the required SELECT statement directly relates to SQL syntax. Other options cause runtime or configuration errors, not syntax errors.

3. Final Answer:

   The SQL file is missing the required SELECT statement -> Option C

4. Quick Check:

   Syntax error = malformed SQL like missing SELECT [OK]

Hint: Syntax errors usually mean SQL is incomplete or malformed [OK]

Common Mistakes:

• Confusing missing test listing with syntax error
• Assuming zero rows cause syntax errors
• Ignoring missing model references

5. You want to create a custom singular test that checks if any user has a NULL email in the users table. Which SQL query should you write in your test file?

hard

A. SELECT * FROM {{ ref('users') }} WHERE email IS NULL

B. SELECT COUNT(*) FROM {{ ref('users') }} WHERE email IS NULL

C. SELECT email FROM {{ ref('users') }} WHERE email IS NOT NULL

D. SELECT * FROM {{ ref('users') }} WHERE email = ''

Solution

1. Step 1: Understand the test goal

   The test should return rows where email is NULL to detect missing emails.

2. Step 2: Choose the SQL that returns rows with NULL emails

   SELECT * FROM {{ ref('users') }} WHERE email IS NULL returns rows only when there are NULL emails (0 rows = pass). COUNT(*) always returns one row, failing even with zero NULLs. IS NOT NULL selects good rows (opposite). = '' checks empty strings, not NULLs.

3. Final Answer:

   SELECT * FROM {{ ref('users') }} WHERE email IS NULL -> Option A

4. Quick Check:

   Return rows with NULL email = SELECT * FROM {{ ref('users') }} WHERE email IS NULL [OK]

Hint: Use SELECT * WHERE column IS NULL to find missing values [OK]

Common Mistakes:

• Using COUNT(*) instead of returning rows
• Checking for empty string instead of NULL
• Selecting non-NULL emails