Databricks 提出 LTAP：在存储层统一事务与分析负载
Databricks 讲了个新存储架构 LTAP，行式列式两层共存，专治 AI 智能体又要事务又要分析的数据访问难题。
Databricks 提出 LTAP 架构，回应 AI 智能体需要同时访问实时事务数据和分析数据的问题。LTAP 在存储层设置热层和冷层：热层以行式格式保存数据供事务访问，冷层以列式格式支持分析读取。每种负载仍由专门的计算引擎独立处理，同一份数据的两种表示被统一在引擎之下。这一设计与过去数十年事务和分析系统分开优化的传统做法不同。
For decades, operational and analytical systems have been optimized separately for good reason. Transactions rely on fast row-based access, while analytics is optimized around columnar storage and broad scans.
But now, AI agents are putting pressure on that boundary. They need to act on live operational data while also using data from the analytical side.
LTAP changes where those workloads meet. It unifies them at the storage layer, with a hotter tier that keeps data in row format for operational access and a cooler tier that holds it in columnar format for analytical reads. Specialized compute can handle each workload independently.
The architectural shift is simple: keep the specialized engine for each job while bringing the operational and analytical representations of the same data together underneath them.
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