I agree, but analytics is orthogonal topic.

Unless you want to use columnar database to retrieve 15 transaction that particular user did month ago...

There is nothing wrong with that use case. It works just fine. It may not have the same latency as an OLTP database but there are many factors (indexing, sort/partition keys, compression, etc) that can return similar performance for those queries.

Just to clarify what you advocate for: you want to run this type of queries on analytical databases (like Snowflake, Redshift etc) to display 10 rows on frontend device?

    select * from transactions where user_id='asdf' and completed_date <'2021-02-01' and completed_date >= '2021-01-01'

Sure. Again the database and data layout make a difference. Snowflake has startup latency since the "warehouse" might not be running but Redshift will do fine, along with many others that are always on.

Redshift has some pretty significant and fairly unpredictable per-query overhead. For example, all queries are compiled, then code is distributed to every node in the cluster, then executed and gathered before returning. And most analytics databases use a scheduled execution model, which can delay execution from a couple seconds to several minutes, depending on database loads.