Granular Usage Attribution for dbt Pipelines With Query Tags
Databricks, Wednesday, July 1st, 2026
Databricks shows how Query Tags enable model-level cost tracking and performance monitoring for dbt pipelines.
This post explains how Query Tags enable detailed cost tracking and performance monitoring for dbt data pipelines on Databricks. The feature automatically injects metadata about each model execution into system.query_history, letting teams attribute warehouse costs at the model level.
By adding custom tags at the profile or model level, organizations can track spend by team, cost center, and environment without modifying SQL.
The post includes a complete open-source reference project for building self-monitoring dashboards and recommends best practices for consistent tagging hierarchies across dbt projects.