Elite data quality doesn’t happen by accident—it’s the result of intentional testing, smart monitoring, and ruthless automation. Thankfully, with dbt, you can build an airtight data quality framework. And with Elementary, you can take that framework to a whole new level of visibility and trust.
Here’s how to level-up your pipelines with dbt + Elementary: built-in tests, custom tests, real-world examples, and advanced observability.
🧱 Built-in dbt Tests – Your First Line of Defense
These are your foundational guards against dirty data.
1. not_null
columns:
- name: user_id
tests:
- not_null2. unique
columns:
- name: order_id
tests:
- unique3. accepted_values
columns:
- name: status
tests:
- accepted_values:
values: ['active', 'inactive', 'pending']4. relationships
columns:
- name: customer_id
tests:
- relationships:
to: ref('dim_customers')
field: id
🛠️ Custom Tests for Business Logic
When the built-ins don’t cut it, you roll your own.
✅ Positive Values Test
{% test positive_values(model, column_name) %}
SELECT *
FROM {{ model }}
WHERE {{ column_name }} <= 0
{% endtest %}
columns:
- name: price
tests:
- positive_values
🗓 Dates Must Be in the Past
{% test past_date(model, column_name) %}
SELECT *
FROM {{ model }}
WHERE {{ column_name }} > current_date
{% endtest %}🔁 Status-Churn Logic
{% test churned_users_have_churn_date(model) %}
SELECT *
FROM {{ model }}
WHERE status = 'churned' AND churn_date IS NULL
{% endtest %}🔍 Using Elementary to Monitor & Visualize Tests
Elementary is a powerful open-source tool that analyzes dbt artifacts (run_results.json, manifest.json) and generates a full data observability report.
🔥 What it does:
Tracks test failures over time
Groups failures by model/test/type
Highlights freshness issues, row count anomalies, and schema changes
Lets you drill into failed rows
Sends Slack/Email alerts on failures
Automatically publishes a sleek web UI or pushes to dbt Cloud/S3
🚀 Tips for Going Pro
Use
severity: warnfor soft failures:
tests:
- unique:
severity: warnRun in CI/CD pipelines (GitHub Actions, GitLab, dbt Cloud).
Track test coverage using
manifest.jsonand visualize it in Elementary.Integrate with Airflow or orchestrators to auto-monitor tests daily.
🎯 Final Thoughts
Combining dbt’s test framework with Elementary’s observability layer creates a best-in-class data quality system:
✅ Built-in tests
✅ Custom macros
✅ Alerts and dashboards
✅ Stakeholder visibility
Whether you're just getting started or scaling out a mature data platform, this duo will help you deliver clean, reliable, and trusted data—consistently.