Databricks has announced the public preview launch of Query Tags, a new capability for Databricks SQL that allows organizations to attach custom business context as key-value pairs to every SQL execution across shared warehouses. The feature addresses a persistent gap in warehouse observability: while Databricks SQL already logs who ran a query and from which tool, it has not previously captured custom metadata such as which team, project, cost center, or application generated a given query. Query Tags fills that gap and stores the metadata directly in the Query History System Table, where it can be analyzed with standard SQL or queried in natural language through Databricks’ Genie interface.
The feature delivers value across three primary scenarios. For partner tools including dbt, Power BI, and Tableau, Query Tags enable automatic propagation of identifiers — such as dbt model names, Power BI report IDs, and Tableau workbook names — into every query without manual configuration. For custom applications hitting warehouses through the SQL Statement Execution API or connectors, developers can attach metadata such as customer ID, application name, or app version at either the connection or statement level. For analysts doing ad-hoc work in the SQL Editor, Notebooks, Dashboards, and Alerts, a single SQL statement sets tags that automatically carry through all subsequent queries in the session.
The practical applications are significant for organizations running shared warehouses across multiple teams. Rather than splitting warehouses by team to track costs — an approach that adds infrastructure complexity and expense — organizations can now attribute shared warehouse costs to specific teams, projects, or cost centers through a single query against the system table. The feature also enables new monitoring capabilities, including identifying which dbt model introduced a performance regression, isolating slow queries tied to a specific Tableau workbook, and separating development from production traffic. Query Tags has already seen adoption across hundreds of customers tagging millions of queries weekly ahead of the public preview announcement.
Databricks said the roadmap for Query Tags includes Power BI automatic tagging enabled by default in the next Power BI release, broader connector support across Go and Node.js, search functionality in the Query History UI, and expansion beyond SQL warehouses to Serverless Notebooks and Jobs.
KEY QUOTES:
“Without having to configure anything, we can map each SQL workload to the dbt model it originates from. With Query Tags we can finally accurately split up warehouse costs by the teams that are running dbt on it.”
Dipesh Bhundia and Dave Couse, Staff Engineering Leads, ASOS
“We moved from one warehouse per team to shared warehouses to cut costs, but lost visibility into which team was driving spend. With Query Tags, we just pass the team name from our Databricks SQL Connector for Python workloads and we have that attribution back — no need to split warehouses again.”
Matthew Haber, DevOps Engineer, Unit21
