I have only one connector – postgres – so I will select it by doing the following: Navigate to a directory where you want to create a project and call: dbt initĭuring execution, this command asks several questions: the name of the project and id of the connector. This takes some time because there are many dependencies, but not too many. Thus, I’ll install dbt for Postgres with: pip install –-user dbt-postgres But here is the catch: there are many different connectors from dbt to other databases, so installing dbt in an isolated environment may not be that beneficial in this specific situation.Īn alternative way is to use Python virtualenv and install dbt there, but for the sake of simplicity, I won’t describe it here. Here’s my interpretation: dbt is a half-declarative tool for describing transformations inside a warehouse.ĭbt doesn’t perform any extractions or loads (as in ELT) it is only responsible for transformations.Ī remarkable fact about dbt: it uses 2 data engineering lingua franca: SQL and YAML.Īs dbt Core is written in Python I would usually install it with pipx. dbt handles turn these select statements into tables and views.” It sounds interesting, but maybe that’s not entirely clear. 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.
I’ve seen more questions and more success stories, so a couple of days ago I decided to try it out. For some time now, I’ve noticed that dbt® is gaining popularity.
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