dbt-expectations for data quality

Introduction

dbt-expectations helps you check if your data is correct and clean. It makes sure your data follows rules you set.

You want to check if a column has no missing values.

You need to make sure numbers in a column are within a certain range.

You want to confirm that values in a column are unique.

You want to test if text columns match a pattern like email format.

You want to automate data quality checks in your data pipeline.

Syntax

dbt

tests:
  - dbt_expectations.expect_column_values_to_not_be_null:
      column: your_column_name
  - dbt_expectations.expect_column_values_to_be_between:
      column: your_column_name
      min_value: 0
      max_value: 100
  - dbt_expectations.expect_column_values_to_be_unique:
      column: your_column_name

Each test is written under the tests section in your dbt model or schema file.

You specify the test name and the column it applies to, plus any extra parameters like min or max values.

Examples

This test checks that the user_id column has no missing values.

dbt

tests:
  - dbt_expectations.expect_column_values_to_not_be_null:
      column: user_id

This test ensures the age column values are between 0 and 120.

dbt

tests:
  - dbt_expectations.expect_column_values_to_be_between:
      column: age
      min_value: 0
      max_value: 120

This test checks that all emails in the email column are unique.

dbt

tests:
  - dbt_expectations.expect_column_values_to_be_unique:
      column: email

Sample Program

This example shows a dbt schema file defining tests for the customers model. It checks that customer_id is never null and unique, and age is between 0 and 120.

dbt

version: 2
models:
  - name: customers
    columns:
      - name: customer_id
        tests:
          - dbt_expectations.expect_column_values_to_not_be_null
          - dbt_expectations.expect_column_values_to_be_unique
      - name: age
        tests:
          - dbt_expectations.expect_column_values_to_be_between:
              min_value: 0
              max_value: 120

Important Notes

dbt-expectations is built on top of Great Expectations, so it uses similar test ideas but fits into dbt workflows.

You can add many types of tests like checking patterns, value sets, or statistical properties.

Running these tests regularly helps catch data problems early.

Summary

dbt-expectations lets you write simple tests to check your data quality.

You add tests in your dbt schema files under the tests section.

Common tests check for nulls, uniqueness, value ranges, and patterns.

Practice

1. What is the main purpose of dbt-expectations in data projects?

easy

A. To create visual dashboards for data

B. To load data into the database

C. To write tests that check data quality in your models

D. To write SQL queries faster

Solution

1. Step 1: Understand dbt-expectations role

   dbt-expectations is a package that helps add tests to check data quality.

2. Step 2: Identify main function

   Its main function is to write simple tests like checking for nulls or uniqueness in data models.

3. Final Answer:

   To write tests that check data quality in your models -> Option C

4. Quick Check:

   dbt-expectations = data quality tests [OK]

Hint: Remember: dbt-expectations is for testing data quality [OK]

Common Mistakes:

• Confusing dbt-expectations with visualization tools
• Thinking it loads or transforms data
• Assuming it speeds up SQL writing

2. Which of the following is the correct way to add a null check test using dbt-expectations in a schema.yml file?

easy

A. tests: - expect_column_values_to_be_null

B. tests: - dbt_expectations.expect_column_values_to_not_be_null

C. tests: - dbt-expectations.expect_column_values_to_not_be_null

D. tests: - expect_column_values_to_not_be_null

Solution

1. Step 1: Recall dbt-expectations test syntax

   In schema.yml, tests are listed by their function name without the package prefix.

2. Step 2: Identify correct test name

   The correct test for checking no nulls is expect_column_values_to_not_be_null.

3. Final Answer:

   tests: - expect_column_values_to_not_be_null -> Option D

4. Quick Check:

   Test names exclude package prefix [OK]

Hint: Use test function names without package prefix in schema.yml [OK]

Common Mistakes:

• Including package name in test list
• Using wrong test name like 'expect_column_values_to_be_null'
• Using hyphens instead of underscores

3. Given this snippet in a schema.yml file:

tests:
  - expect_column_values_to_be_between:
      min_value: 10
      max_value: 100

What does this test check for the column?

medium

A. Values are between 10 and 100 inclusive

B. Values are unique

C. Values are not null

D. All values are exactly 10 or 100

Solution

1. Step 1: Understand the test name

   The test expect_column_values_to_be_between checks if values fall within a range.

2. Step 2: Interpret parameters

   Parameters min_value: 10 and max_value: 100 mean values must be between 10 and 100, including these limits.

3. Final Answer:

   Values are between 10 and 100 inclusive -> Option A

4. Quick Check:

   Between test checks range limits [OK]

Hint: Between test checks inclusive range by default [OK]

Common Mistakes:

• Thinking values must be exactly min or max
• Confusing with null or uniqueness tests
• Assuming exclusive range instead of inclusive

4. You wrote this test in your schema.yml:

tests:
  - expect_column_values_to_be_unique_or_nulls

But dbt throws an error when running tests. What is the likely cause?

medium

A. The test name is incorrect; it should be expect_column_values_to_be_unique_or_nulls

B. The test name is incorrect; it should be expect_column_values_to_be_unique_or_null

C. The test requires parameters min_value and max_value

D. The test cannot be used in schema.yml files

Solution

1. Step 1: Check test name spelling

   The correct test name is expect_column_values_to_be_unique_or_null without the trailing 's'.

2. Step 2: Understand test usage

   This test does not require parameters and is valid in schema.yml files.

3. Final Answer:

   The test name is incorrect; it should be expect_column_values_to_be_unique_or_null -> Option B

4. Quick Check:

   Exact test names matter for dbt-expectations [OK]

Hint: Check exact test function names for typos [OK]

Common Mistakes:

• Adding extra characters to test names
• Assuming parameters are needed for all tests
• Thinking some tests can't be in schema.yml

5. You want to ensure a column email in your users model has no nulls and all values match a simple email pattern using dbt-expectations. Which test setup in schema.yml is correct?

hard

A. tests:
- expect_column_values_to_not_be_null
- expect_column_values_to_match_regex:
regex: '^[\w.-]+@[\w.-]+\.\w+$'

B. tests:
- expect_column_values_to_be_null
- expect_column_values_to_match_regex:
regex: '^[\w.-]+@[\w.-]+\.\w+$'

C. tests:
- expect_column_values_to_not_be_null
- expect_column_values_to_be_unique

D. tests:
- expect_column_values_to_not_be_null
- expect_column_values_to_match_regex:
regex: '[e-mail]'

Solution

1. Step 1: Identify null check test

   Use expect_column_values_to_not_be_null to ensure no null emails.

2. Step 2: Identify regex pattern test

   Use expect_column_values_to_match_regex with a regex matching email format.

3. Step 3: Evaluate options

   tests: - expect_column_values_to_not_be_null - expect_column_values_to_match_regex: regex: '^[\w.-]+@[\w.-]+\.\w+$' correctly combines both tests with a valid regex pattern.

4. Final Answer:

   tests: - expect_column_values_to_not_be_null - expect_column_values_to_match_regex: regex: '^[\w.-]+@[\w.-]+\.\w+$' -> Option A

5. Quick Check:

   Null check + regex pattern = correct test setup [OK]

Hint: Combine null check and regex for pattern validation [OK]

Common Mistakes:

• Using expect_column_values_to_be_null instead of not null
• Using a fixed string instead of regex pattern
• Omitting the regex test for pattern check