Databricks pitches LTAP as a new foundation for agentic applications
As enterprises rush to build AI agents that can reason over business data and take action, Databricks argues that the long-standing practice of separating operational and analytical data systems is turning into a liability. That separation, the cloud-based data warehouse provider says, is becoming increasingly strained as AI agents require simultaneous access to live operational data and historical context to make decisions and take actions in real time, unlike humans, who traditionally can work with data that is minutes or hours old. At its annual Data + AI Summit, the data warehouse provider introduced Lake Transactional and Analytical Processing (LTAP), a new architecture designed to unify transactional and analytical data on a single storage layer. The new approach, according to Databricks, differs from traditional online transaction processing (OLTP) and online analytical processing (OLAP) architectures, which typically store operational and analytical data in separate systems. Tr