Databricks claims to have solved decades-old data pipeline problem hindering AI agents

For decades, data professionals have struggled with managing both operational and analytical databases in a unified approach that doesn't introduce latency and performance degradation. Agents made the problem structural. A system that reasons continuously and acts on live data cannot tolerate a pipeline between itself and the information it needs to act on.

At the Data + AI Summit on Tuesday, Databricks announced two products aimed at collapsing that infrastructure. Lakehouse//RT delivers millisecond query latency directly on governed Delta and Iceberg tables, eliminating the dedicated real-time serving tier that enterprises have maintained alongside their lakehouses. LTAP, short for Lake Transactional/Analytical Processing, stores Postgres-native transactional data in Delta and Iceberg format from the point of write, removing the ETL pipelines that have connected operational and analytical systems for decades.

Reynold Xin, co-founder of Databricks, described a simpler data stack as "the holy grail for agents" in a briefing, arguing that as users vibe code more applications, the agents reasoning analytically on top of those apps need the underlying infrastructure out of the way to move fast. "The agents really prefer a much simpler stack, because they can move way faster," he said. LTAP bets on storage-layer unification where HTAP tried engine convergence.

Many vendors have tried various approaches over the decades to unify analytical and transactional data. Back in 2014, analyst firm Gartner coined the term HTAP, an acronym that stands for Hybrid Transactional/Analytical Processing as a way to describe vendors that attempted to unify the two types of databases. Vendors including MemSQL (now known as SingleStore), SAP HANA, and Oracle's MySQL Heatwave are among many HTAP vendors in the market.

LTAP is Databricks' answer to HTAP, using the Lakebase architecture to unify data at the storage layer rather than the engine level. Lakebase is Databricks' serverless cloud-based PostgreSQL database service that became generally available in February. "HTAP to us is kind of more of a failure of the industry rather than a success," Xin said.

The LTAP approach goes to the storage layer instead of the query layer. Lakebase previously stored Postgres data in Postgres format on object storage, requiring conversion before the Lakehouse's analytical engines could use it efficiently. With LTAP, transactional data lands directly in Delta or Iceberg format, sharing the same copy that analytical workloads read.

Postgres remains the transactional engine. Spark and the Lakehouse remain the analytical engine. "The whole point is, hey, you use the best tool for the job at the query engine level, we just make sure underlying storage is a single copy of the data," Xin said.