Store test failures for analysis in dbt - Cheat Sheet & Quick Revision

Recall & Review

beginner

What is the purpose of storing test failures in dbt?

Storing test failures helps you analyze and understand why data quality checks failed. It allows you to track issues over time and fix data problems effectively.

intermediate

How can you capture test failure details in dbt?

You can capture test failure details by configuring dbt to write test results to a table or by using custom SQL tests that insert failure records into a dedicated table for analysis.

intermediate

What is a common method to store test failures for later analysis in dbt?

A common method is to create a table that stores rows which fail tests, using SQL tests that insert failing rows into this table during dbt runs.

beginner

Why is it helpful to analyze stored test failures over time?

Analyzing stored failures helps identify recurring data issues, track improvements, and prioritize fixes, improving overall data reliability.

advanced

What dbt feature can you use to automate storing test failures?

You can use dbt's custom tests combined with post-hook SQL statements to automatically insert failure details into a table after tests run.

What does storing test failures in dbt help you do?

A Delete old data

B Speed up data loading

C Create new data models

D Analyze and fix data quality issues

Storing test failures helps analyze and fix data quality issues by keeping track of what failed.

Which method can be used to store test failures in dbt?

A Export data to Excel manually

B Create a table and insert failing rows using SQL tests

C Only use built-in dbt logs

D Run dbt without tests

Creating a table and inserting failing rows via SQL tests is a common way to store failures for analysis.

What is a benefit of analyzing stored test failures over time?

A Make data models slower

B Increase data storage costs

C Identify recurring data problems

D Hide errors from users

Analyzing failures helps identify recurring problems so you can fix them.

Which dbt feature helps automate storing test failures?

A Custom tests with post-hook SQL

B dbt seed files

C dbt snapshot

D dbt docs generation

Custom tests combined with post-hook SQL can automate inserting failure details into tables.

What type of data is stored when capturing test failures?

A Rows that did not pass the test

B All rows in the dataset

C Only metadata about the test

D User login information

You store the rows that failed the test to understand what data caused the failure.

Explain how you can store test failures in dbt for later analysis.

Think about using custom SQL tests and tables.

Why is it important to analyze stored test failures over time?

Consider the benefits of tracking problems.

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