Introducing Databricks Query Runner: Transforming data into insight

We know data is at the heart of every great decision. But let’s be honest—more often than not, accessing and analyzing that data usually means wrangling SQL, waiting on analysts, and juggling way too many tools. All that time could otherwise be spent innovating, scaling, and launching what’s next.

This is why we are thrilled to announce our newest integration with Databricks. By combining Atlassian’s robust collaboration tools with Databricks’ powerful unified data, we are empowering organizations to unlock the full value of their proprietary data through seamless, AI-powered experiences.

Atlassian’s Databricks Query Runner is your new agent for instant, AI-powered insights, accessed within Atlassian Rovo. This agent is powered by AI/BI Genie, a conversational analytics tool that empowers users to ask natural language questions and receive instant information and dashboards from their data. Built on compound AI and integrated deeply with the Databricks platform, Genie enables real-time, self-service analytics. Now, anyone on your team can query structured data in Databricks to get faster, smarter, and more democratized access to data-driven insights—no SQL or data science expertise required.

What can Databricks Query Runner do?

• Ask Anything, Instantly: Just type your question in Rovo, and Databricks Query Runner will fetch accurate, traceable insights from your Databricks environment.
• Stay in the Flow: All your data analysis happens right where you’re working. No more switching tabs or losing your train of thought.
• Empower Everyone: Now, anyone on your team—not just the data pros—can uncover insights and make data-driven decisions.
• Enterprise-Grade Security: Built on Atlassian’s trusted cloud platform, your data stays secure, compliant, and reliable.

See more examples of the Databricks Query Runner in action:

Example 1: 

• Scenario: A Product Manager explores new product opportunities.
• Use Case: “Which customer segments are most engaged with Feature X over the last 90 days?”
• Outcome: Databricks Query Runner reveals usage trends by segment, guiding roadmap prioritization.

Example 2:  

• Scenario: A marketing team reviews campaign results in Confluence.
• Use Case: “What was the ROI and conversion uplift from our most recent campaign?”
• Outcome: Databricks Query Runner provides campaign ROI, feeding data directly into retrospectives.

Example 3: 

• Scenario: Support leaders want to understand churn signals from incoming tickets.
• Use Case: “Are ticket volume or resolution times linked to higher customer churn this quarter?”
• Outcome: Databricks Query Runner analyzes churn risk factors and flags at-risk accounts.

Example 4:

• Scenario: Finance teams document operational reviews in Confluence.
• Use Case: “How did cloud infrastructure costs trend after scaling Feature Y?”
• Outcome: Databricks Query Runner provides cost breakdowns, aligning engineering with financial planning.

Currently, the agent is available within Confluence; Jira will be available later this year.

Building the Future of Work—Together

The launch of Databricks Query Runner is just the beginning of the data sharing partnership we announced with Databricks earlier this year. Using the Delta Sharing protocol, the partnership unlocks new data sharing capabilities from Atlassian Analytics. With Databricks, we are instantly doubling the amount of data to pull insights from, enabling customers everywhere to turn data into action effortlessly.

Next, we will be launching the Databricks Connector, which will enable users to easily search for Databricks tables and dashboards—all within Rovo! The Databricks Connector will be available later this year. 

The future of work is here.