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Connection profiles in dbt Developer Hub are essential components that contain the necessary information for connecting to a data platform when using dbt from the command line (CLI). These profiles facilitate the interaction between dbt and the data platform by providing the required credentials and connection details.
dbt run --profiles-dir [path_to_your_profiles.yml]
The above code is an example of how to run dbt from the CLI using a specific profiles.yml file. The path to the profiles.yml file is provided as an argument to the --profiles-dir option.
When dbt is run from the CLI, it reads the dbt_project.yml file to find the profile name. It then searches for a profile with the same name in the profiles.yml file. The profiles.yml file typically exists outside of the dbt project to prevent sensitive credentials from being checked into version control.
dbt run --profiles-dir [path_to_your_profiles.yml]
The code above demonstrates how to specify the directory of the profiles.yml file when running dbt from the CLI. This is particularly useful when the profiles.yml file is located outside of the dbt project directory.
By default, dbt searches for the profiles.yml file in the ~/.dbt/ directory. However, this behavior can be overridden using the DBT_PROFILES_DIR environment variable or the --profiles-dir command-line option.
export DBT_PROFILES_DIR=[path_to_your_profiles_dir]
The above code is an example of how to set the DBT_PROFILES_DIR environment variable to a specific directory. This will override the default location where dbt searches for the profiles.yml file.
The dbt init command is a helpful tool for setting up a connection profile quickly. It prompts the user for connection information and then adds a profile to the local profiles.yml file, or creates the file if it doesn't already exist.
dbt init [project_name]
The code above is an example of how to use the dbt init command to create a new dbt project. The project name is provided as an argument to the init command.
dbt Cloud has the capability to connect with a variety of data platform providers. These include AlloyDB, Amazon Redshift, Apache Spark, Databricks, Google BigQuery, Microsoft Fabric, PostgreSQL, Snowflake, and Starburst or Trino.
No code example required for this section.
No code is required to illustrate this point as it pertains to the compatibility of dbt Cloud with various data platform providers, rather than a specific coding implementation.