[mslot]

OLAP stores are relatively fast at answering a single query on a large data set, but basically none of them can handle high throughput with subsecond response times (e.g. when the whole country checks statistics for their own postcode at 4pm).

OLTP stores are relatively bad at aggregating across a lot of data.

Analytics dashboards with many users, a lot of ever-changing data, and many different views exist in a gray area between OLAP and OLTP often referred to as real-time analytics or operational analytics. The queries are usually somewhat lighter / less ad-hoc / more indexed than in OLAP, but there can be hundreds or thousands of them per second with different filters and aggregations.

There are some specialized real-time analytics databases like Druid. Citus (used in the article) allows you to run such workloads at scale on PostgreSQL.