Store test failures for analysis in dbt - Mini Project: Build & Apply

Store test failures for analysis

📖 Scenario: You work in a data team that uses dbt to build and test data models. You want to capture the details of test failures so you can analyze and fix data quality issues more easily.

🎯 Goal: Create a dbt model that stores the results of test failures in a table for further analysis.

📋 What You'll Learn

Create a source table with sample test failure data

Add a configuration variable to filter failures by severity

Write a dbt model that selects failures based on the configuration

Output the filtered failures for review

💡 Why This Matters

🌍 Real World

Data teams use this approach to track and analyze data quality test failures over time, helping them prioritize fixes.

💼 Career

Knowing how to store and filter test failures is important for data analysts and engineers to maintain reliable data pipelines.

1

Create a source table with test failure data

Create a source table called test_failures with these exact columns and values: test_name (string), failure_reason (string), severity (string). Insert these rows exactly: ('null_check', 'Null values found', 'high'), ('unique_check', 'Duplicates found', 'medium'), ('value_range', 'Values out of range', 'low').

dbt

# Create the source table test_failures with the specified rows
# Your code here

Hint

Use CREATE TABLE and INSERT INTO statements with the exact column names and values.

2

Add a configuration variable for severity filter

Create a dbt variable called severity_filter and set it to the string 'high' to filter failures by severity.

dbt

create table test_failures (
    test_name varchar,
    failure_reason varchar,
    severity varchar
);

insert into test_failures (test_name, failure_reason, severity) values
('null_check', 'Null values found', 'high'),
('unique_check', 'Duplicates found', 'medium'),
('value_range', 'Values out of range', 'low');

# Define the severity_filter variable below
# Your code here

Hint

Use dbt Jinja syntax to set the variable severity_filter to 'high'.

3

Write a dbt model to select failures by severity

Write a dbt model SQL query that selects all columns from test_failures where severity equals the variable severity_filter.

dbt

create table test_failures (
    test_name varchar,
    failure_reason varchar,
    severity varchar
);

insert into test_failures (test_name, failure_reason, severity) values
('null_check', 'Null values found', 'high'),
('unique_check', 'Duplicates found', 'medium'),
('value_range', 'Values out of range', 'low');

{% set severity_filter = 'high' %}

select * from test_failures where severity = '{{ severity_filter }}'
# Your code here

Hint

Use a SELECT statement with a WHERE clause comparing severity to the variable severity_filter using Jinja templating.

4

Output the filtered test failures

Add a print statement to output the results of the dbt model query that selects failures with severity equal to severity_filter.

dbt

create table test_failures (
    test_name varchar,
    failure_reason varchar,
    severity varchar
);

insert into test_failures (test_name, failure_reason, severity) values
('null_check', 'Null values found', 'high'),
('unique_check', 'Duplicates found', 'medium'),
('value_range', 'Values out of range', 'low');

{% set severity_filter = 'high' %}

select * from test_failures where severity = '{{ severity_filter }}'

# Print the filtered failures below
# Your code here

Hint

Use the dbt log function to print the filtered severity value.

Practice

1. What does setting store_failures = true in a dbt test configuration do?

easy

A. It sends an email notification when a test fails.

B. It skips the test execution entirely.

C. It deletes the rows that fail the test from the source table.

D. It saves the rows that fail the test for later analysis.

Solution

1. Step 1: Understand the purpose of store_failures

   This setting controls whether dbt saves the rows that fail a test.

2. Step 2: Identify the effect of setting it to true

   When set to true, dbt stores failing rows in a table for analysis.

3. Final Answer:

   It saves the rows that fail the test for later analysis. -> Option D

4. Quick Check:

   store_failures = true means save failing rows [OK]

Hint: Remember: store_failures saves failing rows for review [OK]

Common Mistakes:

• Thinking it skips tests instead of saving failures
• Assuming it deletes data on failure
• Confusing it with notification settings

2. Which of the following is the correct way to enable storing failures in a dbt test config?

easy

A. config(store_failures == true)

B. config(store_failures: true)

C. config(store_failures = true)

D. config(store_failures = "true")

Solution

1. Step 1: Recall dbt config syntax

   dbt uses Python-like syntax with keyword arguments inside config().

2. Step 2: Identify the correct assignment

   The correct syntax uses equals sign and boolean true without quotes.

3. Final Answer:

   config(store_failures = true) -> Option C

4. Quick Check:

   Use equals and boolean true without quotes [OK]

Hint: Use equals sign and true without quotes in config() [OK]

Common Mistakes:

• Using colon instead of equals sign
• Using double equals (==) instead of single equals
• Putting true in quotes making it a string

3. After running a dbt test with store_failures = true, which SQL query will show the failing rows for a model named orders and test unique?

medium

A. SELECT * FROM dbt_test__orders__unique

B. SELECT * FROM orders WHERE test = 'unique' AND status = 'fail'

C. SELECT * FROM dbt_failures.orders_unique

D. SELECT * FROM test_failures.orders_unique

Solution

1. Step 1: Recall naming convention for failure tables

   dbt stores failures in tables named dbt_test__model__test.

2. Step 2: Apply model and test names

   For model 'orders' and test 'unique', the table is dbt_test__orders__unique.

3. Final Answer:

   SELECT * FROM dbt_test__orders__unique -> Option A

4. Quick Check:

   Failure table = dbt_test__model__test [OK]

Hint: Use dbt_test__model__test naming to find failure tables [OK]

Common Mistakes:

• Assuming failure data is in original model table
• Using incorrect table prefixes
• Trying to filter failures in source tables

4. You set store_failures = true in your test config, but after running tests, no failure tables appear. What is the most likely cause?

medium

A. The test did not find any failing rows, so no failure table was created.

B. You must manually create failure tables before running tests.

C. The store_failures setting only works for schema tests, not data tests.

D. Failure tables are stored in a different schema and need to be queried there.

Solution

1. Step 1: Understand when failure tables are created

   Failure tables are only created if the test finds failing rows.

2. Step 2: Check if test found failures

   If no failures, no failure table is generated.

3. Final Answer:

   The test did not find any failing rows, so no failure table was created. -> Option A

4. Quick Check:

   No failures means no failure table [OK]

Hint: No failure rows means no failure table created [OK]

Common Mistakes:

• Thinking failure tables must be pre-created
• Assuming store_failures only works for some test types
• Looking in wrong schema without verifying failures

5. You want to analyze failing rows from multiple tests on the customers model. Which approach best helps you combine all failure data for analysis?

hard

A. Manually export failure tables and merge them outside the database.

B. Create a union query combining all dbt_test__customers__* failure tables.

C. Run each test again with store_failures = false to avoid duplicates.

D. Use the original customers table and filter rows with errors.

Solution

1. Step 1: Identify failure tables for each test

   Each test creates a failure table named dbt_test__customers__testname.

2. Step 2: Combine failure tables using SQL UNION

   Use a UNION query to combine all failure rows from these tables into one result set.

3. Final Answer:

   Create a union query combining all dbt_test__customers__* failure tables. -> Option B

4. Quick Check:

   Union failure tables to analyze all failures together [OK]

Hint: Use UNION on all failure tables for combined analysis [OK]

Common Mistakes:

• Disabling store_failures loses failure data
• Merging outside database is inefficient
• Filtering original table misses failure details