![]() ![]() 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. Use the profile to define functions for your scripts.For some time now, I’ve noticed that dbt® is gaining popularity. oovy), it will be executed along with any script that you run. profile followed by the designation of the corresponding language to this directory (for example. You can also see them in the Project tool window under Scratches and Consoles/DataGrip Consoles. The scripts are stored in the Configuration directory under consoles/ide. This opens the IDE Scripting Console tab in the editor, where you can type code and execute it using Ctrl+Enter. Once the required option is located, click it to run the console. Press Ctrl+Shift+A and type IDE Scripting Console. Some information and examples are available in this gist. However, you can use any scripting language that is compliant with JSR 223, for example, Python, Ruby, Clojure, and so on. With access to the IntelliJ platform API, you can think of it as a lightweight alternative to a plugin, which adds or modifies some behavior of the IDE.īy default, it supports scripts written in JavaScript and Groovy. ![]() The IDE Scripting Console can be used to write simple scripts that automate DataGrip features and extract various information. ![]()
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