oin us for an invited talk by Stefan Grafberger, Software Engineer at Snowflake, on how AI SQL can bridge relational database systems and unstructured data. Tuesday, 17 November, at 14:15 in AUD4.

Abstract:
The growth of large language models (LLMs) has transformed how organizations interact with data. Modern enterprises now store vast volumes of unstructured content, such as documents, images, audio, and video, alongside traditional structured tables. This talk explores some of the core concepts behind semantic query processing and how modern database architectures like Snowflake bridge the gap between relational execution engines and unstructured data analysis

As part of the SQL Datatypes team, we work on the FILE and VECTOR data types that make these capabilities possible. In this talk, we will examine how Snowflake approaches building our system for unstructured data querying at scale with AI SQL. We will also break down the mechanics of executing AI functions within standard SQL queries and review key query processing optimizations designed to make semantic operations fast and cost-effective.

Bio:
Stefan Grafberger is a Software Engineer at Snowflake, working on improving unstructured data processing as a member of the SQL Datatypes team. Prior to Snowflake, he completed his Ph.D. in data management for machine learning at the University of Amsterdam and BIFOLD / TU Berlin. He holds a Master's degree from TU Munich, where he also focused on database systems. He previously interned with Microsoft Gray Systems Lab, Amazon Research, and Oracle Labs. His research has been repeatedly published in top

The talk takes place on Tuesday, 17 November, at 14:15 in AUD4.