Apparently, Nothing Happened in Database History Between 1986 and Monday

SingleStore challenges Databricks’ portrayal of LTAP as a new response to AI agents, arguing that its distributed SQL platform has supported combined transactional and analytical processing for years through its Universal Storage architecture. It contends that row- and column-oriented storage reflect optimization tradeoffs rather than a requirement for separate systems, citing features such as hash indexes, subsegment access, row-level locking, selective joins, and upserts that allow its columnstore tables to handle both large scans and low-latency operational work. While agreeing that AI agents require immediate access to live transactions, historical records, vector search, and application updates, SingleStore says these needs have long existed in fraud detection, trading, logistics, and other real-time applications. It cites deployments at Armis/ServiceNow and a major bank as examples of production systems handling high-volume ingestion, real-time analytics, search, and transactions, with reduced pipeline complexity and low query latency. The company also describes its Zero-Copy data fabric and Smart Attach capabilities as a way to isolate agent and analytics workloads across compute clusters without duplicating data, and argues that governance catalogs alone cannot eliminate latency between operational and analytical systems.

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