data.gm.com Event June 2026

Power BI & Azure Databricks – Interactive Analytics on the Lakehouse

This session demonstrates how Power BI can connect to and visualize data stored in Azure Databricks, enabling interactive analysis of large datasets. We’ll provide a 200-level overview of Power BI’s capabilities, then a live demo of creating dashboards using Databricks as the data source. Attendees will learn how to configure the Azure Databricks connector in Power BI and build reports on Delta Lake tables. The goal is to show how Databricks’ high-performance engine can be leveraged by business-friendly Power BI tools, bringing big data insights to end-users without complex code.

Intro to Genie Code

Learn how GM and other customers use Databricks Genie Code to enable natural language queries on GM data, giving non-data professionals self-service access to the insights they need. The session will cover what capabilities are available today, walk through a real example, include a live demo, and leave time for Q&A on your own self-service analytics needs.

Power BI & Azure Databricks – Interactive Analytics on the Lakehouse (Advanced)

This session demonstrates how Power BI can connect to and visualize data stored in Azure Databricks, enabling interactive analysis of large datasets. We’ll provide a 200-level overview of Power BI’s capabilities, then a live demo of creating dashboards using Databricks as the data source. Attendees will learn how to configure the Azure Databricks connector in Power BI and build reports on Delta Lake tables. The goal is to show how Databricks’ high-performance engine can be leveraged by business-friendly Power BI tools, bringing big data insights to end-users without complex code.

AI Access & Databricks

Learn how agents like Glean integrate with AI-ready data products like Genie Spaces and Vector Search endpoints deployed in domain-aligned AI Access Layer workspaces in the GM Data Mesh.

Azure Databricks 101 for Business

An overview of the modern data platform powering data.gm.com. Aaron will walk through what's available on the platform, the kinds of business problems it can solve, and how to engage the team when you need support on a data project. This session is designed for two audiences: 1) Business stakeholders who could benefit from data.gm.com services but don't consider themselves data practitioners 2) GM data professionals who haven't yet explored the data mesh and the broader data.gm.com offering.

GM's AI Context Layer: How Atlan Engineers Context for Trusted AI Agent Results

Everyone is racing to ship AI agents; few can trust the answers. This session shows how GM can use Atlan's Context Engineering Studio and Context Agents to build a governed context layer that sits across warehouses, BI, and operational systems, then feeds consistent, testable context into any agent. We'll walk through how AI bootstraps most of the semantic model from GM's existing dashboards and SQL, how domain experts capture the last-mile business logic, and how evaluation suites and traces validate agent behavior before rollout. Attendees will leave with a concrete blueprint for engineering GM's context layer so agents answer like GM does—not like a generic LLM.

Visual Studio Code & GitHub Copilot AI-Assisted Development for Databricks

A 200-level session geared towards developers, showing how Visual Studio Code and GitHub Copilot can improve productivity when connecting to Azure Databricks. We will showcase the Databricks extension for VS Code, which lets developers connect directly to the Databricks ecosystem and demonstrate how GitHub Copilot can aid in the rapid building of line-of-business applications connecting to Databricks.

Thinking Like a Data Scientist (Before You Code)

This session focuses on the work that happens before analysis begins—ideation, problem framing, and hypothesis development. Learn how to turn business questions into well-defined data science projects by clarifying the problem, defining scope, and developing testable hypotheses that drive meaningful insights.

How We Build on the GM Data Stack

This workshop shows how GM builds, governs, and discovers data on Databricks, with Anomalo for data quality, Atlan for cataloging, and Glean for discoverability — and how engineers accelerate that work with Cursor and the AI Dev Kit. Through a live development session, Dave Olds (GM) and Steven Muschler (Databricks) will demonstrate AI-assisted development directly against GM's trusted, governed data ecosystem.

Framing the idea - business (From "we should use AI" to a fundable idea)

Learn best practices for Business and Solution Envisioning—how to turn a vague "we should use AI" concept into a clearly defined, value-driven, and fundable business idea with measurable outcomes. Led by Microsoft & Databricks.

Data Transfers (internal & external) EDT/IEX

Learn how GM securely moves data across teams and partners through our External Data Transfer and Inbound Data eXchange processes. We'll break down real scenarios, the governance controls that keep us compliant by design, and the automations that cut risk while speeding collaboration. Join us to learn practical ways to streamline data exchanges, protect sensitive information, and keep GM secure.

Framing the idea - technical (Scope it before you build it: matching ideas to the right pattern)

Learn how to scope AI and data ideas correctly before building—matching use cases to the right technical patterns, architecture, and constraints to ensure feasibility and fast iteration. Led by Microsoft & Databricks.

"From Lakehouse to Launchpad" - A Databricks Apps presentation

Checkout a live Databricks App that unifies yard, dock, in-transit, and carrier data into one operations cockpit. AI-driven ETA forecasting flags at-risk shipments hours before they slip; Genie lets ops ask "where's PO-12345?" in plain English and get the answer with full lineage. Dispatcher, plant manager, and exec each get the right view of the same governed data — no spreadsheets, no portal sprawl, no waiting on a BI ticket. Sensor to dispatch decision in seconds, end-to-end on Databricks Apps with Unity Catalog governance.

Glean Agentic Development

In this session, you will learn the basics of building Glean agents. You can expect to walk through Agent Builder step by step, learn how to turn a simple idea into a working personal agent, and discover core concepts like triggers, inputs, actions, and testing. By the end of the session, you'll have the tools to build your first agent and know how to keep experimenting safely in your own workspace.

Immuta - Aata Access Admin as a Data Steward

Discover AI Tools at GM

Discover AI Tools at GM is a fast-paced, high-level session designed to help you unlock the power of GM's growing AI toolbox. You'll learn what each tool—Glean, Copilot Chat, AskGM, and Slack AI—does best, and how to quickly choose the right one for your day-to-day work. Through a live Glean demonstration, you'll see how to use chat, search, and agents, connect to available data sources, and understand how privacy and permissions keep your information secure. You'll leave with practical guidance, links to ongoing help, and the confidence to start using AI to boost your productivity at GM right away.

Information Lifecycle Management and Data Archiving

Information lifecycle management (ILM) guides data from creation to retention and disposal. In this presentation, I'll show how data archiving fits into ILM by moving inactive information into secure, cost-efficient storage while keeping it accessible when needed.

AI Governance & Compliance

AI governance at GM and why it is important.

Genie, Glean & Agentic AI

What we've done - where we're going?

AI.GM and working smarter with AI

We will introduce the content and resources on the new ai.gm and discuss the critical thinking behaviors we have to motivate in order for people to use AI (and data) well and safely. We will also introduce the principles and practicalities of the AI culture we're building together at GM.

Atlan - Data products & Data Contracts

This presentation explains how data products and data contracts work together to deliver reliable, reusable data. We'll clarify what makes a good data product, break down the key parts of a data contract (schema, data quality, SLAs, ownership, and governance), and show practical examples of how contracts set clear expectations between data producers and consumers. You'll leave with a straightforward understanding of how to design, document, and govern data products using contracts so they are easier to find, trust, and use across the organization.

Usage & Costing

An intelligent advisor that delivers insights into Databricks cost and usage, identifying opportunities to optimize spend. It helps eliminate waste, provides organization-level cost breakdowns, improves understanding of overall spend, supports cost forecasting, and highlights platform stickiness and growth trends - offering a comprehensive, holistic view of our Databricks ecosystem.