Databricks' Lakehouse//RT Kills the Hidden Tax on Real-Time Data

For years, the dirty secret of enterprise data platforms was a hidden tax buried in every real-time dashboard: a separate serving layer sitting alongside your lakehouse, holding a stale copy of your data, with its own pipelines, its own security rules, and its own failure modes. Databricks is now taking direct aim at that architecture with the launch of Lakehouse//RT, a real-time data warehouse built natively into the lakehouse that promises millisecond query latency without moving a single byte of data.

Announced at the Data + AI Summit, Lakehouse//RT is powered by Reyden, a new compute engine built for the concurrency and latency demands of modern agentic enterprises, and is now available in Beta. The announcement was co-authored by Databricks co-founders Nong Li, Shoumik Palkar, Shant Hovsepian, Mostafa Mokhtar, and Reynold Xin.

The Tax You Were Already Paying

For years, enterprises that needed low latency at high concurrency had one option: stand up a separate real-time serving layer alongside the lakehouse. But that serving layer brings vendor lock-in, increased infrastructure costs, fragmented governance, and data that's never truly real time because it's always a copy , leaving enterprises with a forced compromise: accept latency or fragment the stack.

Databricks frames this as a three-way cost: you pay in data duplication (extracting data from open formats like Delta and Iceberg into proprietary storage), you pay in governance (re-defining security policies in a second system that inevitably drifts from the first), and you pay in engineering (someone has to own, debug, and run that pipeline). And after all of that, the serving layer still can't run all your queries , the moment a query gets complex, with joins or window functions, or the data gets big, it collapses.

The timing is also driven by a new class of consumer: AI agents. Agents are always-on, reasoning in loops, and their ability to act depends entirely on their ability to query complex enterprise data fast. Lakehouse//RT was built to eliminate that compromise, querying Delta and Iceberg tables directly in the governed lakehouse, giving AI agents and humans access to fresh, complete, and trusted data without copying or moving it.