<p>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.</p> <p>This talk is about closing that gap.</p> <p>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.</p> <p>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.</p> <p>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.</p>