Databricks claims to have addressed a foundational infrastructure challenge that has plagued data teams for decades. The problem centers on managing operational and analytical databases without introducing latency or performance losses. AI agents have made this friction acute. These systems must reason continuously and act on live data, but traditional data pipelines introduce delays that make real-time decision-making impossible.
At the Data + AI Summit, Databricks unveiled two products designed to eliminate this bottleneck. Lakehouse//RT delivers millisecond query latency directly on governed Delta and Iceberg tables. This removes the need for a separate real-time serving tier that most enterprises have maintained alongside their data lakehouses. The second product, LTAP (Lake Transactional/Analytical Processing), uses Postgres-native transactions to unify operational and analytical workloads in a single system.
The approach targets a specific pain point. Enterprises typically maintain three separate infrastructure layers: an operational database for transactional workloads, a data warehouse for analytics, and increasingly, a real-time serving layer for AI agents. Each handoff introduces complexity, cost, and latency. Databricks argues that consolidating these into a unified lakehouse eliminates unnecessary data movement and the operational burden of synchronizing across systems.
For AI agents specifically, this matters because they cannot afford millisecond delays. An agent making decisions about fraud detection, inventory, or customer support needs access to current data without waiting for batch pipelines to refresh. Millisecond query latency on governed tables means agents can query live production data while maintaining security and compliance controls that governed tables provide.
The announcement reflects a broader shift in how enterprises think about data infrastructure. Rather than building specialized systems for each workload type, companies now seek unified platforms that handle transactions, analytics, and real-time serving in one place. Delta Lake and Apache Iceberg have emerged as the table formats enabling