Environment management (dev, staging, prod) in dbt - Cheat Sheet & Quick Revision

Recall & Review

beginner

What is the purpose of having separate environments like dev, staging, and prod in dbt?

Separate environments help keep work organized and safe. <br>Dev is for building and testing new changes. <br>Staging is for final testing before going live. <br>Prod is the live environment where real data is used.

beginner

How does dbt use profiles.yml to manage different environments?

The profiles.yml file stores connection details for each environment. <br>It tells dbt where to run models for dev, staging, or prod. <br>You switch environments by changing the target in dbt commands.

beginner

Why should you avoid running untested models directly in the prod environment?

Running untested models in prod can cause errors or bad data. <br>It can break reports or dashboards that people rely on. <br>Testing in dev and staging helps catch problems early.

intermediate

What is a common workflow for promoting changes from dev to prod in dbt?

1. Develop and test models in dev. <br>2. Deploy to staging for final checks. <br>3. After approval, deploy to prod. <br>This keeps data safe and reliable.

beginner

How can you use dbt commands to specify which environment to run?

Use the --target flag with dbt commands. <br>Example: dbt run --target staging runs models in the staging environment. <br>This tells dbt which profile settings to use.

What environment in dbt is typically used for final testing before production?

A Production

B Development

C Staging

D Testing

Staging is the environment used for final testing before deploying to production.

Which file in dbt holds the connection details for different environments?

A profiles.yml

B dbt_project.yml

C settings.yml

D environment.yml

The profiles.yml file contains connection details for dev, staging, and prod environments.

How do you tell dbt to run models in the production environment?

A dbt run --env prod

B dbt run --target prod

C dbt run --profile prod

D dbt run --environment production

The --target flag specifies the environment, so 'dbt run --target prod' runs models in production.

Why is it important to have a dev environment separate from prod?

A To save money on cloud costs

B To avoid using version control

C To speed up production queries

D To test changes without affecting live data

Dev environment allows testing without risking live production data.

What is the risk of skipping staging and deploying directly from dev to prod?

A Potential data errors and broken reports

B No risk at all

C Faster deployment

D Better collaboration

Skipping staging can cause errors in production and affect data quality.

Explain the roles of dev, staging, and prod environments in dbt and why each is important.

Think about how you would safely make changes to a recipe before serving it to guests.

Describe how you would use dbt commands and profiles.yml to switch between environments.

Imagine choosing which kitchen to cook in by telling dbt where to connect.