Open Tables, Shared Truth: Architecting a Multi-Engine Lakehouse

In the modern data landscape, we often hear the phrase "single source of truth." But as many data engineers know, the reality behind that phrase is often a complex web of data copies, inconsistent metrics, and redundant governance. The problem isn’t processing data. It’s where truth lives. For over a decade, we’ve been solving the wrong problem in data engineering. We’ve optimized compute. We’ve scaled storage. We’ve built faster pipelines. And yet— we still don’t trust our data. This blog is not about introducing another analytics engine or tool. It’s about challenging a design flaw we’ve collectively normalized—and showing how modern lakehouse architectures are finally fixing it. The Problem We Stopped Questioning Let’s start with an uncomfortable truth.Most organizations today have: The same dataset copied multiple times The same metric producing different results Governance logic re-implemented across systems And yet, we confidently say: “We have a single source of truth.” But do w