On migrations, there's a .Net tool called Grate that I tend to use for schema migrations... I don't use all the features, but it works well... using a migration stack in a repository for deployments and a similar tool is IMO more reliable than magic comparison tools or hand migrations in practice. You should defensively write your migrations as much as possible so that re-runs are relatively safe, though the tool helps to handle this.

One bit not mentioned, and particularly useful in more modern RDBMS with JSON binary expressions in the database are to leverage JSON columns and avoid joins altogether for a lot of use cases. There are a lot of times where you have variance of sub-information, or other data where table normalization and joins work against you. Even with indexes, joins are costly, especially under load at scale with millions of simultaneous users. You can avoid a lot of this by simply having that sub-table information inside a JSON field with the row in question.

For example, logs and notes related to a specific field. Variable transaction data (paypal vs amazon vs google payments), where the logs/details from the API aren't something that really needs to be in a separate table but related to the transaction.

Another would be something like a classifieds site where many fields are repeated, but sub-fields can vary dramatically by the type of item or category.

Knowing how/when to leverage denormalization and JSON can be one of the most impactful things you can do in terms of performance in practice, short of falling back to a search database (Elastic, Quickwit, etc), which can also be practical depending on your needs, but adds complexity.

Similarly, knowing how your datagase uses certain types of data/serialization... for example UUIDv7 if you don't mind storing creation time (utc) of a record, or COMB if using say MS-SQL in particular... the serialization of said field in practice helps in terms of understanding how indexes update and impact performance.

I do wish the guide was expanded a bit with lots of specific examples and details... a lot of it is hand-wavy blurbs.