dbt-metamodels 0.1.0

Define your dbt models in yaml

dbt-metamodels

Define your dbt models in YAML using metamodels - a powerful way to generate SQL models from macro calls defined in your schema files.

Overview

dbt-metamodels extends dbt to allow you to define models directly in your schema.yml files using a metamodel field. Instead of writing separate SQL files, you can reference macros that generate your SQL code, making your dbt project more maintainable and DRY.

Installation

pip install dbt-metamodels

Or using uv:

uv add dbt-metamodels

Usage

1. Define a Macro

First, create a macro that generates your SQL. For example, in macros/demo_model.sql:

{% macro demo_model(v) %}
select {{ v }} as id
{% endmacro %}

2. Define Models in schema.yml

Instead of creating separate .sql files, define your models in schema.yml using the metamodel field:

version: 2

models:
  - name: my_first_dbt_model
    description: "A starter dbt model"
    metamodel: demo_model(1)
    columns:
      - name: id
        description: "The primary key for this table"

  - name: my_second_dbt_model
    description: "A starter dbt model"
    metamodel: demo_model(2)
    columns:
      - name: id
        description: "The primary key for this table"

3. Use Metamodels Alongside Regular Models

You can mix metamodels with regular SQL models. For example, my_final_dbt_model.sql can reference metamodel-generated models:

{{ config(materialized='table') }}

with source_data as (

    select * from {{ ref('my_first_dbt_model') }}
    union all
    select * from {{ ref('my_second_dbt_model') }}

)

select * from source_data

How It Works

When dbt reads your project files:

1. The plugin intercepts the file reading process
2. It scans schema.yml files for models with a metamodel field
3. For each metamodel definition, it automatically generates a corresponding .sql file
4. The generated SQL wraps your macro call in {{ }} syntax
5. dbt then processes these generated files as if they were regular SQL models

Example Project Structure

metamodels_demo/
├── dbt_project.yml
├── macros/
│   └── demo_model.sql          # Macro definition
├── models/
│   └── example/
│       ├── schema.yml          # Model definitions with metamodels
│       └── my_final_dbt_model.sql  # Regular SQL model
└── profiles.yml

Benefits

• DRY Principle: Reuse macro logic across multiple models
• YAML-First: Define models alongside their documentation
• Flexibility: Mix metamodels with regular SQL models
• Maintainability: Update model logic in one place (the macro)

Requirements

• Python >= 3.8
• dbt-core >= 1.5.0

License

MIT

Contributing

Contributions are welcome! Please feel free to submit a Pull Request.

Links

• Homepage
• Repository
• Issues