Hacker News Reader

Comment by makeitdouble | Hacker News Reader

It sounds like the author is calling a kind of materialized/persisted view "denormalized tables". The actual DB tables stay untouched and fully normalized.

It sure makes sense to love them, views are great. I don't know why they need a new name.

> Transformation tools such as dbt (Data Build Tool) have revolutionized the management and maintenance of denormalized tables. With dbt, we can establish clear relationships between table abstractions, create denormalized analytics datasets on top of them, and ensure data integrity and consistency with tests.

View on HN

Fully concur with this! It succinctly sums up the article! There really isn't much meat there!

There was me assuming it was about normal form, you know, what everyone else means when they talk about 'normalising' in a database context...

Which even more confusingly almost applies but the opposite way around:

> create denormalized analytics datasets on top of them

A common pattern is to have source data 'warehoused' or whatever you want to call it, and then build your, er, normalised schema on top of that.

One of main reason to normalize data is aim to eliminate duplication, and find "the source data" by properly designing the relationships of tables.

By source data I meant as originally supplied, that's perhaps not normal and which you have no control over.

> It sure makes sense to love them, views are great. I don't know why they need a new name.

in very many cases, old things get new names so that more people can share in the claim that they invented what has been in fact merely re-invented

merely re-invented

*rediscovered

As a thought experiment. Was the original thing invented or discovered?

Many things are re-invented/rediscovered from first principles. In maths, some people prefer to think maths are created, and some prefer to think in terms of discovering/uncovering sth that was already there.

It’s complicated. Databases have entities called “views” and “materialized views” that have a specific meaning in that ecosystem. dbt let’s you define views in the abstract sense, but they’re implemented using a variety of different database primitives, like tables, temp tables, common table expressions, and views. dbt calls them “models”.

Sounds like a typical workflow in any BI tool. I wish the author would have taken a moment to explain how Glean is any different.

The debate over normalized/denormalized has to do with how authoritative online data should be stored. What you do with derived datasets is not really contentious; do whatever you want.

Reply on news.ycombinator.com