houseoflkp.blogg.se - Execute command activity stage data stage versions

#Execute command activity stage data stage versions how to#

We will see how the customer_orders table is created from the source tables. # Applies to all files under models/marts/core/ marts: We have set the models under models/marts/core/ to materialize as tables.

This can be overridden in dbt_profiles.yml. Materialization is a variable that controls how dbt creates a model. In this file, you can define the profile to be used and the paths for different types of files (see *-paths). The profiles.yml file can contain multiple profiles for when you have more than one dbt project. We can have multiple targets, which can be specified when running dbt commands. The target variable defines the environment. We have defined the warehouse connection details at /simple_dbt_project/profiles.yml. profiles.ymlĭbt requires a profiles.yml file to contain data warehouse connection details. Let’s set the warehouse connections and project settings. We will go over the models, snapshots, and tests folders in the below sections. These macros can be reused across our project.

macros: Dbt allows users to create macros, which are sql based functions.

This is typically used to store small mapping data.

data: We can store raw data that we want to be loaded into our data warehouse.

They will not be run by dbt but can be copied into any tool of choice. sql files found in this folder will be compiled to raw sql when you run dbt compile. In the simple_dbt_project folder you will see the following folders. This will provide you with a sample project, which you can modify. You can also create a dbt project using dbt init. The DBT_PROFILES_DIR environment variable tells dbt to look for the profiles.yml file in the current working directory.

The dim_customers model can then be referenced in other model definitions.īy default dbt will look for warehouse connections in the file ~/.dbt/profiles.yml. This model depends on the models stg_eltool_customers and stg_eltool_state. The file dim_customers.sql represents the model named dim_customers. The names of models created by dbt are their file names.Į.g. These models can depend on other models, have tests defined on them, and can be created as tables or views. In dbt, we work with models, which is a sql file with a select statement. dbt does the T in ELT (Extract, Load, Transform) processes. Then the raw data is transformed into usable tables, using SQL queries run on the data warehouse.ĭbt provides an easy way to create, transform, and validate the data within a data warehouse. In an ELT pipeline, the raw data is loaded(EL) into the data warehouse. If you are keen to understand why dbt is widely used, please read this article.

#Execute command activity stage data stage versions how to#

If you are a student, analyst, engineer, or anyone in the data space and are curious about what dbt is and how to use it.