Upcoming Events

Modern data lakehouses depend on Apache Iceberg tables staying compact, performant, and cost‑efficient. But keeping them that way manually is tedious and error‑prone. In this live office hours session, Starburst expert Lester Martin will walk through practical approaches for automating Iceberg table maintenance and keeping production workloads running smoothly.

Topics will include:

• The various types of table maintenance activities 
• The consequences of not performing table maintenance
• The benefits of automating vs. manually scheduling maintenance activities

We’re heading to Google Cloud Next to talk about a problem every data team knows: the dashboard backlog.

As AI moves into production, teams need more than static reports. They need systems that can query, reason over, and act on trusted data across the enterprise. 

Stop by Booth #4319 to see how Starburst helps organizations move from dashboards to conversational analytics and production-ready AI.

Apache Iceberg enables you to manage data changes in your lakehouse with the same safety and control teams expect from modern software development. But most organizations only use a fraction of their capabilities.

In this 90-minute hands-on session, we’ll go beyond basic snapshots and time travel to explore how Iceberg features like rollbacks, branching, and tagging enable safer experimentation, easier recovery, and more controlled data deployments.

AI is no longer a question of if—but how fast you can deliver real outcomes without compromising control.

For financial institutions, the challenge isn’t access to AI. It’s turning fragmented, governed data into something usable to create business impact, while operating within complex regulatory and multi-country environments. But it doesn’t have to take years.

In this session, Starburst breaks down how leading financial institutions can launch production-ready AI use cases in just 90 days–without replatforming their entire data estate.

In this session, Kiran Gourish shares Scout24’s journey to Starburst, where autoscaling and enhanced query logging capabilities helped reduce monthly costs to approximately $8,000 while improving workload transparency and governance.

Kiran will also demonstrate how Scout24 built an internal MCP server integrated with ChatGPT, enabling users to query Starburst through conversational AI — eliminating the need for direct UI access and expanding analytics accessibility across the organization.

In this office hours session, we’ll explore how to promote curated datasets into AI-ready data products on Starburst. We’ll enhance business-oriented metadata through a combination of human effort and AI generation. Then we’ll compare the quality of the AI agent’s output before and after enhancing the business metadata. That metadata is the context that will allow the generic GenAI models to be more precise and reduce hallucinations.