Most real-time analytics data is a time series in practice: events with a timestamp, queried by recent windows, grouped into buckets (per minute, per hour, per day). The shape of the workload determines the shape of the storage: data is partitioned by date, sorted by timestamp inside each partition, and queries usually filter by a recent range before doing any other work.

Time-series-specific databases (InfluxDB, TimescaleDB, Prometheus) optimize for this shape, but a column-oriented OLAP engine like ClickHouse® handles time series natively as long as the sort key starts with a time column. You get the same partition pruning, sorted scans, and incremental aggregations without leaving SQL.

Time-series queries are also where materialized views earn their keep: rolling a million raw events per second into per-minute aggregates is the single most common pattern in user-facing analytics.