Why Store test failures for analysis in dbt? - Purpose & Use Cases

The Big Idea

What if you could turn messy test failures into clear insights with just a simple table?

The Scenario

Imagine running many data quality tests manually and writing down each failure in a notebook or spreadsheet.

Later, you want to find patterns or fix recurring issues, but the notes are scattered and inconsistent.

The Problem

Manually tracking test failures is slow and error-prone.

You might miss important details or lose track of when and why a test failed.

This makes it hard to analyze trends or improve data quality over time.

The Solution

Storing test failures automatically in a structured table lets you easily query and analyze them.

You can track failure history, identify common problems, and prioritize fixes efficiently.

Before vs After

✗ Before

Run tests -> Copy failures to spreadsheet -> Search manually

✓ After

Create failure table -> Insert failures automatically -> Query failures with SQL

What It Enables

You can quickly spot data issues, track improvements, and make data quality a continuous, measurable process.

Real Life Example

A data team stores all test failures in a table and finds that a specific source system causes 70% of errors, so they focus their efforts there.

Key Takeaways

Manual tracking is slow and unreliable.

Automated storage of failures enables easy analysis.

Improves data quality by making issues visible and actionable.