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

**TL;DR:** Databricks says it solved the decades-old data pipeline problem that's been slowing AI agents

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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. 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 with VentureBeat, arguing that as users vibe code more applications, the agents reasoning analytically on top of those apps need the under

Source: VentureBeat

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Last updated: June 16, 2026.

Additional context: early-cycle stories often look bigger in headlines than in day-to-day impact. The useful move is to identify the smallest set of facts that would change your decision, then wait for those facts to land.

Additional context: early-cycle stories often look bigger in headlines than in day-to-day impact. The useful move is to identify the smallest set of facts that would change your decision, then wait for those facts to land.