6/22/2023 0 Comments Datagrip python![]() ![]() Of course, it won’t be that easy in real systems, but just imagine that we have a more complex data structure. I delete everything from the models/example directory and create a new file called user.sql there. First modelįirst off, I will define the most simple model possible: a user model. Two customers, one post, one comment – nothing fancy, but already enough to start my experiments. Now I will put some data into my DB just to have something to test my model on. Just for the sake of realism, I added several not entirely necessary fields like created_at. If you prefer a visual representation of tables, here it is: Each post has an author (customer), and each comment has an author (customer) and a post. It’s a straightforward schema with three tables: customer, comment, and post. Test schemaĬonsider the following DDL: CREATE TABLE IF NOT EXISTS customerĬreated_at TIMESTAMP WITH TIME ZONE DEFAULT now() NOT NULLĪuthor BIGINT NOT NULL REFERENCES customer,Ĭreated_at TIMESTAMP WITH TIME ZONE DEFAULT now() NOT NULL, Now it’s time to write the first example. This file contains a declarative description of models, tests, and types. Later on, it will be kept in our storage as a view, materialized view, or other related entity.īesides the SQL model, we should also have a schema.yml file. It is just a single SQL file containing a single SELECT statement. A model is an atomic entity of dbt describing a model of our domain. Now it’s time to understand what the model is. models is the directory with the description of models we will work with.dbt_project.yml is the description of the project’s global settings.This lets us understand what the most important parts are: ![]() Given that I’ve used demo-project as the project name, the following file structure will be generated: demo-projectĪlso, it will create (or update) the file ~/.dbt/profiles.yml, where database connection settings are stored.Īs we can see, most of the project is just empty directories. 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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