Upcoming Events

In this webinar, we’ll explore how AI is reshaping how organizations access, interact with, and act on enterprise data, moving beyond scheduled reports to dynamic, conversational analysis that serves everyone from analysts to executives.

Through an interactive demo, you will walk away with a practical understanding of what replacing static BI with AI could look like — and how to harness that shift to deliver faster answers, shared insights, and a data-empowered organisation.

For more than a decade, organizations have relied on dashboards, reports, and BI tools to guide decision-making. But as AI moves from experimentation into production, many companies are discovering that a data strategy built for dashboards can’t power AI-driven decisions. 

The challenge isn’t the models anymore. It’s getting AI access to the right data with the business context needed to turn insight into action. In this session, Starburst CEO Justin Borgman explores how enterprise intelligence is evolving from static reporting to dynamic, AI-driven decision systems. He’ll outline what organizations need to make that shift: a federated, governed data foundation that allows AI to access and understand data wherever it lives. 

 

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

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.

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.