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Metabase provides business analytics, and this list of "common mistakes" is weighted towards "choices which get in the way of business analytics".

For example:

> 1. Polluting your database with test or fake data

> [...] By polluting your database with test data, you’ve introduced a tax on all analytics (and internal tool building) at your company.

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The end of this article is particularly weird. Is it really suggesting that a good general rule is to optimise for business metric queries (which sounds like something that would generally run daily during off peak hours or ad hoc when someone needs the data) over the most commonly run reads/updates (which sounds like something that will happen multiple times per minute for every active user)?

I feel like I'm missing something because that seems insane to me.

Consider the source. The barber is suggesting optimizing for haircuts.

To your point, many of these could be addressed by making an analytics database copy of the transactional database, for example scrubbing test data and removing soft deletes in your etl.

From my experience with metabase, this makes it easier to use anyway but it means you have to maintain an etl.

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