Databricks

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. https://t.co/27BUTQxngm