Using Variables vs. target in dbt for Dynamic Schema Selection

Introduction

Managing multiple environments in dbt—such as development, staging, and production—can pose a challenge when it comes to changing schemas based on the environment. While dbt’s built-in target object offers a straightforward way to switch schemas automatically, sometimes you need extra flexibility that vars (variables) can provide. In this article, we’ll explore both approaches, discuss their advantages and limitations, and guide you on choosing the right method for your use case.

The Problem

Suppose you have a dbt model that needs to be executed against different source schemas:

? A production schema (prod_schema)

? A staging schema (staging_schema)

? Possibly other schemas for testing or ad hoc analyses

If you hardcode the schema name in your sources.yml file, you’d have to manually update it every time you switch environments:

This manual process is time-consuming and error-prone. Instead, dbt offers two methods to dynamically select a schema:

1. Using target — Relies on the environment configuration in your profiles.yml.

2. Using vars — Allows you to pass in a schema name as a variable at runtime.

Approach 1: Using target

How It Works

target is a built-in dbt object that represents your current execution environment. By referencing target.schema in your sources.yml, dbt automatically picks up the schema specified in your profiles.yml based on the environment you’re running in:

Your profiles.yml might look like this:

When you switch the target in your dbt commands (e.g., dbt run --target staging), dbt automatically updates the schema to staging_schema.

Advantages of Using target

1. No extra variables required: dbt selects the schema based on your active environment in profiles.yml.

2. Simplicity: With proper environment setup, you can seamlessly switch between dev, staging, and prod without modifying your code.

3. Ideal for standard multi-environment setups: If your schema names match your environment names, target is an out-of-the-box solution.

Limitations of Using target

1. Less flexible for mid-environment schema changes: If you want to run the same models against different schemas within the same environment, target alone won’t help.

2. Pre-defined in profiles.yml: You must define schema names in your profile, which might be restrictive if you need on-the-fly changes that aren’t accounted for in the profile.

Approach 2: Using vars

How It Works

If you need more granular control or want to override schemas without modifying your profile, you can use vars. Here, you reference a variable in your sources.yml:

? var('source_schema', 'prod_schema') uses 'prod_schema' as a default if source_schema is not explicitly passed.

At runtime, override it by passing a variable:

This tells dbt to use staging_schema for that run.

Advantages of Using vars

1. High flexibility: Perfect for scenarios where you want to run the same model against different schemas on the fly—useful in A/B testing or multi-tenant models.

2. Explicit control: You decide the schema at runtime, rather than relying solely on environment-based settings.

Limitations of Using vars

1. Manual overhead: You must pass the variable each time you run dbt unless you set up defaults carefully.

2. Risk of misconfiguration: If you forget to pass the variable, or if your defaults aren’t correct, it can lead to unintended behavior.

When to Use target

? Single schema per environment: If each environment has its own schema and that’s all you need, target is the simplest solution.

? Standard dev → staging → prod pipeline: Minimal overhead and seamless environment switching.

When to Use vars

? Multiple schemas in the same environment: If you need to test or run your model against multiple schemas on the fly, vars is more suitable.

? Ad hoc testing or A/B testing: Quickly override the schema without updating your profiles.yml.

Conclusion

Both target and vars enable dynamic schema selection in dbt, but they serve different purposes:

1. Use target for a standard multi-environment workflow: dev, staging, and production. It’s automatic, defined in your profiles.yml, and requires no extra effort per run.

2. Use vars for on-demand flexibility: running the same models on different schemas within the same environment or quickly testing out alternative schemas.

Understanding these two approaches—and when to use each—will help you build more robust, flexible dbt projects without constantly editing YAML files or changing your code.

Have questions or tips on managing schemas dynamically in dbt? Let’s discuss in the comments!

#dbt #DataEngineering #ETL #SQL #DataAnalytics #DataPipeline