Querying Everything Everywhere All at Once: Supervaluationism for the Agentic Lakehouse

TL;DR

This paper introduces a novel multi-branch querying system for lakehouse data architectures, utilizing supervaluationary semantics to answer questions across data versions, addressing challenges in agentic analytics.

Contribution

It presents a new query path and system that enable cross-branch data querying in lakehouses using supervaluationism, a novel semantic approach.

Findings

Prototype demonstrates effective multi-branch querying

Open source implementation available as baseline

Addresses limitations of traditional OLAP systems

Abstract

Agentic analytics is turning the lakehouse into a multi-version system: swarms of (human or AI) producers materialize competing pipelines in data branches, while (human or AI) consumers need answers without knowing the underlying data life-cycle. We demonstrate a new system that answers questions across branches rather than at a single snapshot. Our prototype focuses on a novel query path that evaluates queries under supervaluationary semantics. In the absence of comparable multi-branch querying capabilities in mainstream OLAP systems, we open source the demo code as a concrete baseline for the OLAP community.