Upcoming Events


Starburst Galaxy, End to End: A Guided Tour of the Modern Data Platform
New to Starburst Galaxy? Or ready to see what’s new? Join Starburst Galaxy expert Bo Myers for a guided, end-to-end tour. Learn how teams move from connected data to trusted insight, all in one modern data platform.
AI Didn't Change Our dbt Project, Just Who Cared About It
In this webinar, Monica Miller will walk through how Starburst’s data team turned an existing Iceberg lake, dbt project, and Trino engine into governed context for an AI agent, and what surprised the team along the way.
Office Hours: How to Test and Improve AI Accuracy
In this 30-minute Office Hours session, Puneet Arora will show you how Starburst helps teams ground AI outputs in governed data products and reliable enterprise data access.
The Data Layer for AI Agents: GPUs, Governance, and Open Architecture
AI agents turn every analytical query into a step in a chain, and GPUs are about to change how fast each step can run. In this panel, hosted by Starburst, Martin Traverso, co-creator of Trino and CTO at Starburst, joins Joe Sack and Chuck Desai from NVIDIA to discuss what GPUs mean for analytics, why converged platforms limit your choice of compute, and how an open data ecosystem lets you put the right compute behind every workload.
Starburst Workshop: Data Products as Code for Governed, AI-Ready Analytics
Starburst expert Fabricio Pimentel will show how Starburst treats data products like software artifacts: defined in YAML, managed through CLI and API workflows, and promoted through reviewable processes across environments.
Workshop: Choosing the Right Python Path for Starburst: From DataFrames to Production Pipelines
Starburst supports a few different ways to work with Python: Ibis and PyStarburst as DataFrame APIs, the Trino Python client for direct SQL, and Python UDFs for custom logic. Python UDFs in Starburst Enterprise are currently a public preview and run in a sandboxed environment, so they are best suited for self-contained custom logic rather than code that depends on external files, network access, or arbitrary Python packages.
In this hands-on workshop, we’ll compare them, then build a real pipeline using the Ibis DataFrame API.
