data

In the first part of this blog series, I described basic dbt® concepts such as installation, creation of views, and describing models. I could have stopped there, but indeed, there are some drawbacks to only using views to build the whole transformation layer in our database. Sometimes we don't really need to use a view and a view may run slowly even in databases oriented toward analytical workflows. I’ll start by giving an overview of ephemeral views. Ephemeral views In some cases, we don't really want to have an entity for a dbt® model, rather we want this model to be inlined in oth

For some time now, I’ve noticed that dbt® is gaining popularity. I’ve seen more questions and more success stories, so a couple of days ago I decided to try it out. But what exactly is dbt anyway? Here is the first phrase you can find in its documentation: “dbt (data build tool) enables analytics engineers to transform data in their warehouses by simply writing select statements. dbt handles turn these select statements into tables and views.” It sounds interesting, but maybe that’s not entirely clear. Here’s my interpretation: dbt is a half-declarative tool for describing transformati