Session

Genie Code: Accelerating Development for Data and AI Teams

Overview

Experience	Virtual
Technologies	Genie

Genie Code accelerates development for more technical users, enabling data scientists, data engineers and others to be so much more effective and efficient in the tasks they need to accomplish. Genie Code delivers AI with enterprise context to the users who need it, in the manner in which they need it. Genie Code builds the analysis for you, not just a query. It uses your enterprise context and business semantics to explain the “why” and points to exactly what happened. Hear from Weston Hutchins, Director of Product Management.

Session Speakers

IMAGE COMING SOON

Ari Kaplan

/Global Head of Evangelism
Databricks

Holly Smith

/DevRel
Databricks

Weston Hutchins

/Director, Product Management
Databricks

Full Summary

Inside Genie: How AI is rewriting how Databricks users build

A Summit Live conversation unpacked how Genie, Databricks' AI assistant for technical users, has evolved from a chatbot into an autonomous coding agent embedded in the workspace.

The shift matters because AI is no longer a novelty on the Databricks Platform. For many users, it has become the default way to create Databricks Notebooks, write SQL in Databricks SQL, build AI/BI Dashboards, and iterate on data work. That evolution is part of a broader trend explored in depth in how AI is transforming data analytics, where workspace-native intelligence is reshaping what practitioners can accomplish in a single session.

The discussion highlighted adoption at scale, why workspace context is a decisive advantage for analytical tasks, and how practitioners are already changing their habits. The headline: more code on Databricks now flows through Genie than is written by hand.

From sidebar helper to autonomous agent

Genie is designed for technical practitioners who live in the Databricks workspace, including data scientists, machine learning engineers, data engineers, and analysts. It now operates as an agent that can carry out multi-step tasks. Users described letting it create tables, author Databricks Notebooks, write SQL in Databricks SQL, and assemble complete AI/BI Dashboards from a prompt describing the data and intent.

Everyday productivity gains featured prominently. You can highlight a line to request a one-line change, press a debug button to let the agent iterate on errors, or tag the exact table you want it to use instead of hoping it guesses correctly. A full-page command center experience is also coming, expanding beyond the current side panel.

Context-aware analytics is the edge

General-purpose coding assistants help with boilerplate, but analytical work benefits from deep context. Genie is wired into the workspace, so it understands your tables, Unity Catalog, lineage, and the assets your colleagues create. That context means far less priming. Ask for a forecast or a churn projection against known Databricks Notebooks, and it can surface the right data automatically.

Learning behavior shows up in practical ways. Repeated patterns are recognized and accelerated, which boosts day-to-day speed. During demos, that same awareness can be awkward when it notices you have queried a table repeatedly, a small price for an assistant that meaningfully grasps your environment.

Adoption has crossed a threshold

Usage has surged. Over 90 percent of Databricks customers now use Genie daily. The product recently passed a milestone where more code on the Databricks Platform is authored with Genie than handwritten, with more than 75 percent of code now flowing through it and growth up roughly 10 times since the start of the year.

Internal usage mirrors the trend. Databricks' data platform team is the heaviest user, a strong signal that the tool serves demanding practitioners, not just casual explorers. Customers report that once they get used to Genie, they do not want to give it up.

When intent replaces syntax

The core story is a change in default behavior. As AI authors most of the code, the practitioner's role shifts to articulating intent, shaping context, and reviewing output. Genie turns that pattern into everyday practice inside the Databricks workspace, reducing setup and letting technical teams move from idea to working artifact in minutes.

FAQ

Genie is for technical users working inside the Databricks workspace. It serves data scientists, ML engineers, data engineers, and analysts. The conversation also referenced Genie for business users and mentioned "Genie 1" in passing, but details focused on Genie's role for practitioners.

Users reported creating tables, authoring Databricks Notebooks, writing SQL in Databricks SQL, and building entire AI/BI Dashboards from a prompt. It supports quick inline edits, iterative debugging, and tagging specific tables to steer results. A good first step is asking Genie what it can do to see concrete examples.

Often not. Because it is integrated with your workspace, it understands your tables, Unity Catalog, lineage, and team assets. That context enables tasks like forecasts or churn analyses with minimal setup, though tagging exact tables helps when precision matters.

Open the side panel in Databricks and start asking questions. The speakers noted a full-page command center experience is coming soon, which will make it even easier to run larger tasks.

Internal usage is broad, and the heaviest user is a member of the company's data platform team. That pattern aligns with customer feedback that, after adopting Genie, teams do not want to work without it.