Why Test severity levels in dbt? - Purpose & Use Cases

The Big Idea

What if you could instantly know which data problems need your attention right now?

The Scenario

Imagine you run a data project where you check your data quality by manually scanning error logs and spreadsheets to find problems.

You get many alerts but can't tell which ones are urgent or which can wait.

The Problem

This manual way is slow and confusing.

You waste time fixing small issues that don't matter much, while missing big problems that break your reports.

Errors pile up and you lose trust in your data.

The Solution

Test severity levels let you label data tests as error, warn, or info.

This helps you focus on fixing the most important problems first.

It organizes your alerts so you know what to act on immediately and what to monitor.

Before vs After

✗ Before

run all tests and check logs manually

✓ After

severity: error\nseverity: warn\nseverity: info

What It Enables

With test severity levels, you can quickly spot and fix the biggest data issues before they cause harm.

Real Life Example

A data analyst uses severity levels to catch critical data mismatches that would break dashboards, while ignoring minor warnings that don't affect decisions.

Key Takeaways

Manual checking mixes all problems together, causing confusion.

Severity levels prioritize data tests by importance.

This saves time and improves data trust.