Databricks says it solved the decades-old data pipeline problem that's been slowing AI agents

The useful part

For decades, data professionals have struggled with the challenge of managing both operational and analytical databases in a unified approach that doesn't introduce latency and performance degradation. 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.

How it works

• 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.
• Reynold Xin, co-founder of Databricks, described a simpler data stack as "the holy grail for agents" in a briefing with VentureBeat, arguing that as users vibe code more applications, the agents reasoning...
• 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.
• "The agents really prefer a much simpler stack, because they can move way faster," he said.

What to take from it

Vendors including MemSQL (now known as SingleStore) SAP HANA and Oracle's<a href="https://venturebe...