Apache Iceberg promises a single table format that any engine can query. But operationalizing that promise – across human analysts, automated pipelines, and now AI agents introduces a new class of problems: dialect fragmentation, workload interference, and the lack of a unified control plane over who queries what, how, and where.

This talk is about closing that gap.

Amit Gilad will explore how Iceberg’s open format and catalog model create the foundation for multi-engine interoperability, and then show what it actually takes to run it in production. Enter QueryFlux: an open-source query router that sits in front of Iceberg-backed engines, translating dialects, routing workloads to the right back end DuckDB, Trino, Spark, or StarRocks – and enforcing policy from a single layer.

But the stakes are rising. Agentic AI systems large language model (LLM)-powered tools that autonomously generate and execute queries against data are becoming real workloads. These agents do not respect engine boundaries, do not know cost constraints, and can saturate a cluster with poorly formed queries. QueryFlux provides the layer they need: a single SQL endpoint that handles translation, routes to the appropriate engine, and applies governance before a single byte of data is scanned.

Amit will walk through routing patterns for human and machine workloads alike interactive vs. batch, ad hoc vs. agent-generated and show how QueryFlux uses Iceberg’s metadata model to make smarter decisions across all of them. Attendees will leave with a concrete architecture for a lakehouse that is ready not just for today’s analysts, but for the AI systems querying their data tomorrow.