dbt-expectations for data quality - Time & Space Complexity

Time Complexity: dbt-expectations for data quality

O(n)

Understanding Time Complexity

When using dbt-expectations to check data quality, it is important to understand how the time to run these checks changes as data grows.

We want to know how the cost of running these tests scales with the size of the data.

Scenario Under Consideration

Analyze the time complexity of the following dbt-expectations test.

    - name: test_not_null
      config:
        severity: error
      test: not_null
      column_name: user_id
      model: users
    

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

Identify Repeating Operations

Look at what the test does repeatedly.

• Primary operation: Scanning each row in the user_id column to check for nulls.
• How many times: Once for every row in the users table.

How Execution Grows With Input

As the number of rows grows, the test must check more values.

Input Size (n)	Approx. Operations
10	10 checks for null values
100	100 checks for null values
1000	1000 checks for null values

Pattern observation: The number of checks grows directly with the number of rows.

Final Time Complexity

Time Complexity: O(n)

This means the time to run the test grows in a straight line as the data size increases.

Common Mistake

[X] Wrong: "The test runs instantly no matter how big the data is."

[OK] Correct: The test must look at every row to be sure there are no nulls, so it takes longer with more data.

Interview Connect

Understanding how data quality checks scale helps you write efficient tests and explain their impact clearly in real projects.

Self-Check

"What if we added a test that checks uniqueness of a column instead of nulls? How would the time complexity change?"

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