September 2026 Summaries

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.

SingleStore Analyst is a conversational analytics feature designed to let non-SQL users query governed business data in plain language, receive charts and explanations, explore follow-up questions, and save live results to dashboards. It operates directly on data in SingleStore or on replicated data from systems such as Postgres, MySQL, Oracle, SQL Server, and MongoDB, with the company emphasizing real-time access, high query concurrency, and avoidance of separate analytical copies or synchronization pipelines. Its governance model centers on curated Domains that restrict accessible tables, users, permissions, and business context, while recording activity for administrative review and using read-only database accounts. A built-in Context Engine combines business rules, learned context subject to administrator approval, a semantic layer, reviewed inferred joins, and trusted SQL examples to improve answer accuracy and query performance. Dashboards retain domain-based access controls and rerun saved SQL against current data rather than storing static snapshots, while planned future capabilities include embedding Analyst into customer applications with tenant isolation.