From Search to SQL: How Glean Tap Databricks Genie for Everyday Analytics

For teams investing in business intelligence and analytics in the AI era, the integration patterns outline a pragmatic path beyond static dashboards, toward agentic workflows that connect what, why, and now what. Organizations evaluating how to modernize their analytics stack can find a practical framework in AI/BI Dashboards and their intelligent analytics capabilities, which shows how structured and unstructured data can be unified into answers that drive action.

Why context is the bottleneck for enterprise AI

Access, skills, and discovery remain persistent blockers. Data lives in hundreds of systems, most employees do not write SQL, and even experts struggle to find the current, trusted artifact. Decisions suffer when users cannot get the right information fast enough.

AI alone does not fix that. Without organizational context, models behave like new hires with no institutional memory, producing shallow or wrong answers. The speakers argued for a rich system of context that spans company knowledge, individual work patterns, and permissions. Feeding models the right context reduces token usage by roughly 30 to 50 percent and improves answer quality, because the model receives only what it needs, when it needs it.

What Glean adds alongside Databricks

Glean indexes structured and unstructured sources and builds two graphs: an enterprise graph that maps entities, projects, relationships, and activity, and a private personal graph per user that captures collaborators, recent work, and writing style. The result is retrieval that is both relevant and permissions aware. Answers are built only from content the user can already access in the source systems.

That context sits under an assistant, an agent platform, and APIs, including MCP. Glean can be a front end, a back end, or a context provider to other tools. Organizational awareness matters for real work, whether the question is vague and human, such as "find that Slack from Kelly a few days ago," or operational and high stakes, such as drafting and escalating a ticket to the right owner. More agentic workflows demand more, and more precise, context.

Four ways Glean and Databricks integrate

• Genie inside Glean Assistant: Natural-language questions route to the appropriate Genie space via MCP. Glean translates intent into governed SQL and returns results alongside relevant context.
   
• Direct SQL to Databricks SQL: Users or Glean agents can send SQL straight to Databricks SQL. When needed, Glean adjusts SQL dialects automatically before execution.
   
• Indexing Databricks objects: Databricks Platform objects, including AI/BI Dashboards and Genie spaces, plus metrics, owners, and descriptions, are indexed and discoverable inside Glean to reduce duplicate work.
   
• Glean context inside Databricks: From Genie or Databricks Notebooks, users can pull unstructured context such as Teams conversations, Jira tickets, call transcripts, or design docs to explain the why behind the numbers.
   

The unifying idea is simple: Databricks excels at what happened. Glean brings the why. Agentic workflows propose, or automate, what to do next.

From insight to action in the real world

The pattern shows up across industries. A top-three global automaker is rolling out Glean to roughly 150,000 users and expects about 300,000 employee hours saved in year one by unifying context from SharePoint, Outlook, Slack, and more with Databricks. A real estate SaaS company has built over 3,000 agents to automate steps beyond insight delivery. An EV manufacturer targets several hundred thousand hours of annual productivity gains by centralizing knowledge and accelerating onboarding.

In each case, progress follows the same arc: bring structured and unstructured context together, expose it through natural language, then move from self-serve answers to automated action.

The shared context layer, not a single interface, unlocks value

Different users will live in different tools. Some will stay in Databricks. Others will prefer an AI assistant. Durable value comes from a shared, permissions-aware context layer that powers both experiences, turning AI from a novelty into a reliable coworker.