The phrase is often overloaded. In practice, real-time analytics requires three things at once: fresh data (sub-second ingestion lag), fast queries (sub-second response over recent windows), and the ability to serve those queries to applications, not just to dashboards.

This rules out most data warehouses, which are tuned for high-throughput batch scans, and most general-purpose databases, which are not designed for the high-fan-out aggregation patterns analytics requires. The architectures that work are usually built on a column-oriented OLAP engine, an ingestion path that bypasses heavy ETL, and a query layer that can fan out responses to many concurrent callers.

Tinybird's product is shaped by this definition: ingest events, build pipes, publish endpoints, watch p99 stay low.