DBT : How to manage multiple environments in DBT ?

When working with data, it’s often necessary to manage multiple environments, such as development, testing, and production, in order to ensure the integrity of the data. DBT provides several features that make it easy to manage multiple environments and keep your data models organized.

One of the key features of DBT for managing multiple environments is the use of profiles. A profile in DBT is a set of configuration settings that define how DBT should connect to your data sources and how it should behave when running commands. You can use multiple profiles in DBT, each with its own set of configuration settings, allowing you to manage multiple environments with ease.

For example, you can create a profile for your development environment, where you may want to use a smaller dataset or test your models before deploying to production. You can also create a profile for your production environment, where you may want to use a larger dataset and ensure that your models are thoroughly tested before deploying.

To use profiles in DBT, you’ll need to create a profiles.yml file in your project’s root directory. In this file, you’ll define the profiles you want to use and specify the configuration settings for each profile.

For example:

default: dev

profiles:
  dev:
    target: dev
    outputs:
      dev:
        type: postgres
        host: aws.redshift.dev.freshers.in
        user: postgres
        password: password
        database: dev
  prod:
    target: prod
    outputs:
      prod:
        type: postgres
        host: aws.redshift.prod.freshers.in
        user: postgres
        password: password
        database: prod

To use a profile when running DBT commands, simply specify the profile using the –profile flag. For example:

dbt run --profile dev

Another useful feature of DBT for managing multiple environments is the use of environment variables. Environment variables allow you to dynamically set values in your DBT models and profiles, making it easy to manage different settings for each environment. For example, you can use environment variables to specify different connection strings for different environments.

To use environment variables in DBT, you’ll need to set the variables in your shell before running DBT commands. For example:

export DBT_DEV_HOST=localhost
export DBT_DEV_PORT=5432
export DBT_DEV_DATABASE=dev

You can then reference these environment variables in your DBT models and profiles using the {{ env() }} Jinja function. For example:

{{ env("DBT_DEV_HOST") }}