It's worth noting that ClickHouse supports both the standard SQL and the extensions.

For example, the standard SQL way to write filtered aggregates is:

    SELECT avg(number) FILTER (WHERE number > 50) FROM numbers(100)

See https://modern-sql.com/feature/filter

But it is only supported by SQLite, PostgreSQL, and ClickHouse. While ClickHouse also provides a shorthand:

    SELECT avgIf(number, number > 50) FROM numbers(100)

The same for DISTINCT:

    SELECT avg(DISTINCT number)
    SELECT avgDistinct(number)

Why filtered aggregates are important? - They allow to implement the "segment comparison" feature in web analytics services.

    WITH Region = 'us' AS segment1, Browser = 'Chrome' AS segment2
    SELECT uniqIf(UserID, segment1), uniqIf(UserID, segment2)
    WHERE segment1 OR segment2

Most of the existing web analytics services are using ClickHouse: CloudFlare Analytics, Microsoft Clarity, Plausible Analytics, PostHog, Splitbee...