DBT provides several commands that allow data teams to run different tasks on their data models, such as dbt run, dbt full-refresh, and dbt test.
In this article, we will discuss the difference between dbt run, dbt full-refresh, and dbt test, their syntax, and provide examples to demonstrate how each command can be used.
The dbt run command is used to build and run the models defined in a DBT project. The dbt run command is used to generate the views and tables that will be used for analysis. The dbt run command takes into account the relationships between models and builds them in the correct order.
Example for dbt run for the specific model my_customer_info_table:
Suppose you have a DBT project with several models, and you want to build and run only the my_customer_info_table model. You can use the –models option to specify the model you want to run.
$ dbt run --models my_customer_info_table
Compiling 1 models ...
The dbt full-refresh command is used to rebuild all of the models in a DBT project. This command is useful when a change has been made to the data source, or if the data source has changed in a way that affects the models.
Example for dbt full-refresh for the specific model my_customer_info_table:
The dbt full-refresh command will rebuild all of the models in your DBT project by default. However, if you want to rebuild only the my_customer_info_table model, you can use the –models option.
$ dbt full-refresh --models my_customer_info_table
Rebuilding all models ...
The dbt test command is used to run tests on the models defined in a DBT project. Tests are used to validate the correctness of the data. The dbt test command runs tests on the models and outputs the results to the terminal.
Example for dbt test for the specific model my_customer_info_table:
The dbt test command will run any tests defined in your DBT project. If you want to run tests only for the my_customer_info_table model, you can use the –models option.
$ dbt test --models my_customer_info_table
Running test for my_customer_info_table
In conclusion, dbt run, dbt full-refresh, and dbt test are three important commands in DBT that are used for building, refreshing, and testing data models. Each command serves a specific purpose and helps data teams automate different tasks in their data transformation and validation processes. By understanding the difference between these commands, data teams can use DBT more effectively to manage their data models.
Note: In these examples, the output from running the dbt run, dbt full-refresh, and dbt test commands has been simplified for clarity. The actual output you see when running these commands may vary depending on the specifics of your DBT project and the models and tests you have defined.
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