Announcing Native Databricks Support in Wren AI

Bringing GenBI to the Modern Lakehouse

Today, we’re excited to announce that Wren AI now integrates natively with Databricks, making it easier than ever for organizations to turn their lakehouse into a fully conversational analytics platform.

As more teams adopt Databricks to power large-scale data engineering, Delta Lake pipelines, and AI workloads, one challenge still remains:

The last-mile problem of analytics

Your data is robust. Your pipelines are modern. But your business teams still struggle to access insights without SQL, dashboards, or long turnaround times.

This is the gap Wren AI solves.

Wren AI: The GenBI Layer on Top of Databricks

With this new integration, teams can now:

• Ask questions in plain English and get SQL-backed answers instantly
• Generate charts and dashboards automatically, without modeling or engineering work
• Use Wren AI’s semantic layer to enforce consistent metrics across teams
• Empower business users—product, finance, operations, marketing—to self-serve insights from Databricks data

Databricks powers the data foundation. Wren AI unlocks the usability layer that brings data to everyone.

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What This Means for Your Organization

With Wren AI + Databricks, you can:

✨ Move faster: Insights that once required dashboards or analysts now take seconds.

✨ Reduce bottlenecks: Data teams stay focused on engineering, not constant reporting requests.

✨ Increase data confidence: Definitions, metrics, and business logic stay consistent across every question and dashboard.

✨ Scale AI-driven decision-making: Every team can explore the lakehouse as naturally as they communicate.

Get Started

Connect Databricks to Wren AI in minutes using our guide:

👉 https://docs.getwren.ai/cp/guide/connect/databricks

We’re excited to bring GenBI to the lakehouse era. As data infrastructure evolves, the next frontier is accessibility—and Wren AI is here to make Databricks data usable, understandable, and actionable for everyone.

This is just the beginning. 🚀