Over the last decade, streaming architectures have largely been built around topic-centric primitives—logs, streams, and event pipelines—then stitched together with databases, caches, OLAP engines, and (increasingly) new serving systems. This approach scales, but it also accumulates architectural debt: duplicated data, fractured "truth," inconsistent guarantees, and rising operational overhead as batch, streaming, and product analytics diverge.

In this talk we introduce an emerging shift we call the Streamhouse: a table-centric streaming architecture that treats tables as the primary primitive, and models "real-time" as freshness tiers rather than separate systems. Conceptually, it extends the lakehouse by making continuous ingestion + continuous maintenance the default, so one copy of data can serve both low-latency and historical workloads with straightforward SQL access.

Then we switch from idea to practice: we'll walk through how platforms evolve from batch refreshes -> "near-real-time" -> hot/warm/cold SLAs. We'll show a pattern we built first for analytics: an OLAP serving layer with federated SQL over tiered data. That solves the initial problem—until you notice the same shape repeating across other workloads that also want "fresh + queryable" canonical data, not just dashboards. The talk follows how this pushes teams to generalize from "OLAP over the lake" to "tiered access as a platform primitive," and what trade-offs you must get right: where state lives, how tier boundaries are defined, how continuous maintenance is paid for, and what consistency guarantees you can realistically promise.

The session is practical but forward-looking: a working mental model for tiered real-time lakehouse systems, plus reference architecture patterns and a decision framework for applying the Streamhouse idea as repeatable architecture rather than a custom stack.