Why everyone is talking about real-time analytics (a note from “the yellow company”)

Real-time analytics, once considered a niche use case, is gaining broader recognition due to its critical role in customer-facing analytics, observability, fraud detection, operational applications, and AI agents, all of which require fresh data and low-latency queries. ClickHouse, a company that has been at the forefront of real-time analytics, emphasizes the importance of openness and clear definitions for real-time analytics databases. They argue that benchmarks should be open, transparent, and reproducible, noting that benchmarks often fail to capture essential production properties like data freshness, continuous ingestion, concurrency, transformation latency, and scalability. Real-time analytics platforms must support scalable ingestion without degrading queries, ensure near-instant data queryability, update transformations incrementally, and provide resource efficiency at scale. The rise in demand for real-time analytics is partly due to agentic workloads, which require a continuous flow of fresh data and high concurrency to function effectively. As the industry evolves, ClickHouse advocates for standardized benchmarks and a shared understanding of the comprehensive requirements for real-time analytics beyond mere latency numbers.