Summary:

• Databricks launches Lakehouse//RT, the real-time Lakehouse powered by Reyden, a new compute engine that delivers millisecond query latency at tens of thousands of concurrent users and agents, directly on governed Delta Lake and Apache Iceberg tables
• With Lakehouse//RT, customers have seen up to 16x better performance than existing real-time serving stacks, with response times as low as 10ms on smaller datasets and sub-100ms performance on larger ones
• Every Lakehouse//RT query runs natively within Unity Catalog’s governance framework with no separate permissions layer, no proprietary formats, and no sync/CDC pipelines, eliminating the cost and complexity of maintaining a separate real-time serving layer alongside the lakehouse

Databricks, a data and AI company, announced Lakehouse//RT, the real-time evolution of the Databricks Lakehouse.Lakehouse//RT allows enterprises to run real-time analytics directly on the governed Delta Lake and Apache Iceberg data, eliminating the need to set up separate serving systems to achieve millisecond performance. Powered by Reyden, a new compute engine built for the concurrency and latency demands of modern agentic enterprises, Lakehouse//RT is now available in Beta.

Delivering the Real-time Lakehouse
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. This leaves enterprises with a forced compromise: accept latency or fragment the stack. For humans, it is a headache. But for agents, it doesn’t work. 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. It queries 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. Its execution engine is designed to support tens of thousands of concurrent users and agents while maintaining consistently low latency. On standard analytical benchmarks, Lakehouse//RT delivers sub-100 millisecond latency at 12,000 queries per second, and customers have seen up to 16x better performance than their existing specialized real-time serving stacks. By removing the need for a separate serving layer, Lakehouse//RT also eliminates the cost, CDC and synchronization pipelines, governance gaps, and proprietary lock-in that come with it.

“Over the past decade, we’ve unified the major workloads of the modern data stack on a single open foundation: data engineering and data science with Spark, and data warehousing with Photon and the Lakehouse,” said Ali Ghodsi, co-founder and CEO, Databricks. “Lakehouse//RT completes the engine spectrum, providing the millisecond speed layer that people want and agents require. Just as we proved that the best data warehouse is a lakehouse, now, the best real-time analytics engine is the lakehouse, too.”

Inside Lakehouse//RT
Lakehouse//RT was built for the specific demands of real-time serving at scale:

• Millisecond latency, at any scale: Reyden’s fully asynchronous execution model delivers response times as low as 10 milliseconds on smaller datasets and 100 milliseconds on larger ones, without latency degrading as throughput climbs into the tens of thousands. And unlike engines optimized only for simple lookups, Lakehouse//RT applies state-of-the-art performance techniques to the full range of analytical complexity
• Open, governed, single system: Every query runs within Unity Catalog’s governance framework, including policies, permissions, and auditing. No separate governance layer to maintain, no gaps between analytical serving and the rest of the enterprise data estate
• Fresh data, zero configuration: Lakehouse//RT queries Delta and Iceberg tables directly, with no proprietary formats, no data copies, and no ingestion pipelines. Point it at any existing table and start querying live data in minutes

Customer Momentum for Lakehouse//RT
“Threat lookup requires consistently low latency, even as usage scales across users and agents,” said Chris Kopek, head, data platforms, Cisco. “What we’re seeing with Lakehouse//RT is millisecond performance on live data with 5x improvement in response time, which creates a path to run those workloads on our lakehouse instead of maintaining a separate serving system.”

“Our platform serves hundreds of queries per second for real-time performance data across our entire client base, so latency and consistency directly impact how customers experience our product,” said Kayvon Raphael, senior director, engineering, Magnite. “With Lakehouse//RT, we’re seeing sub-200 millisecond performance on our core dashboard queries, consistently. Being able to achieve that while keeping everything governed inside our own data lake massively reduces the complexity of managing our data pipeline and servicing of consumer applications.”

Availability
Lakehouse//RT is now available in Beta.