dbt-expectations for data quality - Cheat Sheet & Quick Revision

Recall & Review

beginner

What is dbt-expectations in the context of data quality?

dbt-expectations is a package for dbt that helps you write tests to check your data quality by defining expectations or rules your data should meet.

intermediate

How does dbt-expectations improve data quality checks compared to basic dbt tests?

dbt-expectations provides a rich set of reusable, parameterized tests that cover common data quality scenarios, making it easier and faster to write thorough tests.

beginner

What is an example of a dbt-expectations test you can use to check for null values in a column?

You can use expect_column_values_to_not_be_null to ensure a column has no null values, which helps catch missing data issues.

beginner

Why is it important to use dbt-expectations tests in your data pipeline?

Using dbt-expectations tests helps catch data problems early, ensures data reliability, and builds trust in your data for decision-making.

beginner

How do you add dbt-expectations to your dbt project?

You add dbt-expectations by installing it as a package in your packages.yml file and then running dbt deps to download it.

What does the dbt-expectations test expect_column_values_to_be_unique check?

A That values in a column are within a range

B That a column has no null values

C That values in a column match a regex pattern

D That all values in a column are unique

This test ensures there are no duplicate values in the specified column.

Which file do you modify to add dbt-expectations as a dependency?

A dbt_project.yml

B packages.yml

C profiles.yml

D schema.yml

The packages.yml file is used to list external dbt packages like dbt-expectations.

What is the main benefit of using dbt-expectations tests over writing custom SQL tests?

A They are reusable and easier to maintain

B They require no configuration

C They run faster

D They automatically fix data issues

dbt-expectations provides reusable tests that save time and reduce errors compared to writing custom SQL tests.

Which dbt-expectations test would you use to check if values in a column fall within a specific set?

A expect_column_values_to_not_be_null

B expect_column_values_to_be_unique

C expect_column_values_to_be_in_set

D expect_column_values_to_match_regex

This test verifies that all values in a column are part of a predefined set.

After adding dbt-expectations to packages.yml, what command do you run to install it?

A dbt deps

B dbt test

C dbt compile

D dbt run

The command dbt deps downloads and installs packages listed in packages.yml.

Explain how dbt-expectations helps maintain data quality in a data project.

Think about how automated checks help in real life to avoid mistakes.

Describe the steps to add and use dbt-expectations in your dbt workflow.

Focus on setup and usage in your project.

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