Beyond the Lakehouse: Extending Databricks’ reach with FME and MCP
Webinar Details
Databricks has become home base for some of the most critical data an organization owns. Putting that data to work with AI is the next step, and also the one that keeps security teams up at night. A single unchecked request can expose sensitive records, inflate token costs, or trigger a compliance fine. Meanwhile, spatial data, legacy systems, and other hard-to-reach sources often sit outside the lakehouse entirely. AI without access to that data is AI without the full breadth of knowledge, and the questions it can’t answer stay unanswered.
The Model Context Protocol (MCP) offers a different path. With FME, data teams can build MCP tools on top of spatial and other hard-to-reach data. Databricks Genie One can then use those tools to answer location-based questions that were never possible before. We’ll also flip it around: build MCP tools on your Databricks lakehouse and use them with any AI assistant. Either way, you decide exactly what AI can see, all without writing code.
Join us to learn how to:
- Build MCP tools on spatial and other hard-to-reach data, and put them to work in Databricks Genie One
- Control precisely what data is exposed to AI and trace every request through job logs
- Read and write Delta Lake tables and add spatial validation to lakehouse workflows
- Manage token spend and open Databricks data to governed use across the organization
Leave with a practical blueprint for putting AI to work on lakehouse data while keeping governance, cost, and choice firmly in hand.