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Databricks says it solved the decades-old data pipeline problem that's been slowing AI agents
For decades, data professionals have faced challenges unifying operational and analytical databases without performance issues. Agents, which require continuous reasoning on live data, highlight the inefficiencies of traditional data pipelines. Databricks has introduced Lakehouse//RT and LTAP to address these problems by collapsing infrastructure. Lakehouse//RT offers millisecond query latency directly on governed Delta and Iceberg tables, eliminating the need for a separate real-time serving tier. LTAP, or Lake Transactional/Analytical Processing, stores Postgres-native transactional data in Delta and Iceberg format from the point of write, removing ETL pipelines. This approach unifies data at the storage layer, unlike previous HTAP solutions that focused on engine convergence. The core engineering challenge is latency, which Lakehouse//RT overcomes with its Reyden compute engine and a caching layer handling row-to-column conversion. Lakehouse//RT provides sub-100ms latency and operates within Unity Catalog's governance framework without data copies. While the problem is recognized, Databricks' agentic AI framing and open-format approach are seen as key differentiators. Analysts note that while Lakehouse's architecture is strong, its latency and reliability must be proven. The move to open formats for transactional writes and direct lake querying is considered significant. For enterprises, especially those leveraging agents, the question shifts from selecting best-of-breed tools to defensible separate systems. The gaps between specialized systems are becoming operational risks for agents, driving consolidation away from separate serving layers. Agent workloads cannot tolerate the latency inherent in traditional data architectures built for human-speed analysis.
