Running tests with dbt test - Deep Dive

Overview - Running tests with dbt test

What is it?

Running tests with dbt test means checking your data models to make sure they are correct and reliable. dbt test runs predefined or custom tests on your data to find errors or unexpected values. This helps catch problems early before using the data for analysis or reports. It is like a safety check for your data pipelines.

Why it matters

Without running tests, data errors can go unnoticed and cause wrong decisions or wasted time fixing issues later. Tests help maintain trust in your data by automatically verifying assumptions and quality. This saves effort and improves confidence when sharing data insights with others. It also speeds up finding and fixing problems.

Where it fits

Before running dbt test, you should know how to build data models with dbt and write SQL queries. After learning tests, you can explore advanced testing strategies, continuous integration, and monitoring data quality in production pipelines.

Mental Model

Core Idea

Running tests with dbt test automatically checks your data models for correctness and quality before using them.

Think of it like...

It's like proofreading a document before sending it out to catch typos and mistakes that could confuse readers.

┌───────────────┐
│  dbt models   │
└──────┬────────┘
       │
       ▼
┌───────────────┐
│  dbt test     │
│ (runs checks) │
└──────┬────────┘
       │
       ▼
┌───────────────┐
│ Test results  │
│ (pass/fail)   │
└───────────────┘

Build-Up - 7 Steps

1

Foundation

What is dbt test command

Concept: Introducing the dbt test command and its purpose.

The dbt test command runs tests defined in your dbt project. These tests check your data models for issues like missing values or duplicates. You run it in your terminal with 'dbt test'. It helps catch data problems early.

Result

dbt runs all tests and shows which pass or fail in the terminal.

Understanding the dbt test command is the first step to automating data quality checks.

2

Foundation

Types of tests in dbt

Concept: Learn about built-in and custom tests in dbt.

dbt has built-in tests like 'unique', 'not_null', and 'accepted_values' you can add to your models. You can also write custom SQL tests for specific rules. Tests are defined in YAML files or SQL files inside your project.

Result

You can specify tests to check different data quality rules easily.

Knowing test types helps you pick the right checks for your data needs.

3

Intermediate

Writing schema tests in YAML

🤔Before reading on: do you think schema tests are written in SQL or YAML? Commit to your answer.

Concept: How to define tests in schema YAML files linked to models.

Schema tests are written in YAML files next to your models. You list columns and attach tests like unique or not_null. For example: models: - name: customers columns: - name: id tests: - unique - not_null This tells dbt to check the 'id' column for uniqueness and no missing values.

Result

dbt knows which tests to run on which columns when you run 'dbt test'.

Using YAML for tests keeps your checks organized and close to your data models.

4

Intermediate

Creating custom SQL tests

🤔Before reading on: do you think custom tests must always check one column or can they check multiple columns? Commit to your answer.

Concept: Writing your own SQL queries as tests for complex rules.

Custom tests are SQL files that return rows when data fails the test. For example, a test to find customers with negative age: SELECT * FROM {{ ref('customers') }} WHERE age < 0 If this query returns rows, the test fails. You save this SQL in the 'tests' folder and reference it in your YAML.

Result

You can check any condition you want beyond built-in tests.

Custom SQL tests give you full flexibility to enforce business rules.

5

Intermediate

Interpreting dbt test results

🤔Before reading on: do you think dbt test stops at first failure or reports all failures? Commit to your answer.

Concept: Understanding the output format and meaning of test results.

When you run 'dbt test', it runs all tests and shows a summary. It lists which tests passed and which failed, including how many rows failed. It does not stop at first failure but runs all tests to give a full report.

Result

You get a clear picture of data quality issues to fix.

Knowing how to read test results helps prioritize data fixes effectively.

6

Advanced

Automating tests in CI/CD pipelines

🤔Before reading on: do you think running dbt test manually is enough for production data quality? Commit to your answer.

Concept: Integrating dbt tests into automated workflows for continuous quality checks.

In production, you automate 'dbt test' in CI/CD pipelines so tests run on every code change or data update. This catches errors early without manual effort. You configure your pipeline to run 'dbt test' after building models and fail if tests fail.

Result

Data quality is continuously monitored and enforced automatically.

Automation ensures consistent data quality and faster feedback loops.

7

Expert

Handling flaky tests and test performance

🤔Before reading on: do you think all dbt tests run equally fast and reliably? Commit to your answer.

Concept: Understanding challenges with test reliability and speed in large projects.

Some tests can be flaky if data changes rapidly or tests depend on timing. Complex custom tests may run slowly on big datasets. Experts optimize tests by limiting scope, using incremental models, or caching results. They also handle flaky tests by retrying or marking them as warnings.

Result

Tests remain reliable and efficient even in complex environments.

Knowing test limitations and optimizations prevents false alarms and slow pipelines.

Under the Hood

dbt test works by compiling your test definitions into SQL queries that run against your database. Built-in tests generate standard SQL checks like counting duplicates or nulls. Custom tests run your SQL directly. The results are collected and interpreted: if any rows are returned, the test fails. dbt manages dependencies so tests run after models are built.

Why designed this way?

dbt was designed to leverage the power of SQL and the database engine for testing, avoiding moving data around. This keeps tests fast and scalable. Using YAML for schema tests separates test logic from SQL code, making tests easier to write and maintain. The design balances flexibility with simplicity.

┌───────────────┐
│ Test YAML     │
│ definitions   │
└──────┬────────┘
       │ compiles
       ▼
┌───────────────┐
│ SQL test      │
│ queries       │
└──────┬────────┘
       │ runs on
       ▼
┌───────────────┐
│ Database      │
│ engine        │
└──────┬────────┘
       │ returns rows if fail
       ▼
┌───────────────┐
│ dbt collects  │
│ results       │
└───────────────┘

Myth Busters - 4 Common Misconceptions

Quick: Does dbt test fix data errors automatically? Commit yes or no.

Common Belief: dbt test automatically corrects data errors it finds.

Reality: dbt test only detects and reports errors; it does not fix data.

Why it matters: Expecting automatic fixes can lead to ignoring test failures and bad data persisting.

Quick: Do all tests run instantly regardless of data size? Commit yes or no.

Common Belief: All dbt tests run quickly no matter how big the data is.

Reality: Some tests, especially custom ones on large tables, can be slow and impact pipeline speed.

Why it matters: Ignoring test performance can cause slow deployments and frustrated teams.

Quick: Does a passing test guarantee perfect data? Commit yes or no.

Common Belief: If all dbt tests pass, the data is perfect and error-free.

Reality: Tests only check what you define; untested issues can still exist.

Why it matters: Relying solely on tests without good coverage can give false confidence.

Quick: Can you write tests that check multiple tables at once? Commit yes or no.

Common Belief: dbt tests can only check one table or model at a time.

Reality: Custom SQL tests can join multiple tables to check complex conditions.

Why it matters: Knowing this expands your ability to enforce cross-table data rules.

Expert Zone

1

Some tests may pass locally but fail in production due to data volume or environment differences.

2

Test failures can be caused by upstream model changes, so understanding dependencies is key to debugging.

3

Using tags and selective test runs helps manage large test suites efficiently.

When NOT to use

dbt test is not suitable for real-time streaming data validation or very high-frequency checks. For those, specialized data monitoring tools or database triggers are better. Also, for extremely complex validations, external data quality platforms may be preferred.

Production Patterns

In production, teams integrate dbt test into CI/CD pipelines with alerting on failures. They use test coverage reports to track quality over time. Some use test result artifacts to create dashboards showing data health trends.

Connections

Unit Testing in Software Development

dbt tests are like unit tests but for data models instead of code functions.

Understanding software unit testing helps grasp why automated data tests improve reliability and speed up development.

Data Quality Management

dbt test is a practical tool within the broader discipline of managing data quality.

Knowing data quality principles helps design better tests that cover critical data issues.

Continuous Integration / Continuous Deployment (CI/CD)

dbt test integrates into CI/CD pipelines to automate data validation on every change.

Understanding CI/CD concepts clarifies how automated tests fit into modern data engineering workflows.

Common Pitfalls

#1 Ignoring test failures and proceeding with data analysis.

Wrong approach:

dbt test
# test fails but user ignores and continues using data

Correct approach:

dbt test
# test fails
# user investigates and fixes data or models before proceeding

Root cause:

Misunderstanding that tests are warnings, not blockers, leads to ignoring critical data issues.

#2 Writing overly broad custom tests that return too many false positives.

Wrong approach:

SELECT * FROM {{ ref('orders') }} WHERE order_date < '1900-01-01' OR order_date IS NULL OR order_date > CURRENT_DATE + 1000

Correct approach:

SELECT * FROM {{ ref('orders') }} WHERE order_date < '1900-01-01' OR order_date IS NULL

Root cause:

Trying to cover too many edge cases in one test causes noise and confusion.

#3 Defining tests in the wrong YAML file or forgetting to link them to models.

Wrong approach:

tests:
- unique:
column_name: id
# but placed outside model schema YAML

Correct approach:

models:
- name: customers
columns:
- name: id
tests:
- unique

Root cause:

Not following dbt's YAML structure causes tests to be ignored.

Key Takeaways

Running tests with dbt test is essential to catch data errors early and maintain trust in your data.

dbt supports built-in and custom tests, letting you check simple rules and complex business logic.

Tests are defined close to models in YAML or as SQL files, keeping your project organized.

Automating tests in CI/CD pipelines ensures continuous data quality without manual effort.

Understanding test results and limitations helps you fix issues efficiently and avoid false confidence.