Models and Transformations

Models are the core building blocks of your dbt project. They define the transformations that turn raw data into analytics-ready datasets using SQL. This guide covers how to create models, manage dependencies, and leverage dbt’s templating capabilities.

What Are Models?

In dbt, a model is a SQL file that defines a transformation. When you run dbt, it compiles these SQL files into executable queries and runs them against your data warehouse, creating views or tables. Models serve three key purposes:

Transform data into useful analytics structures
Document transformations with clear SQL
Create dependencies between different data assets

Each model typically results in a single table or view in your data warehouse.

Creating Your First Model

A model is simply a .sql file in your models/ directory. Let’s start with a basic example:

-- models/staging/stg_customers.sql
SELECT
  id as customer_id,
  first_name,
  last_name,
  email,
  date_joined
FROM {{ source('jaffle_shop', 'customers') }}

When you run dbt run, this SQL gets compiled and executed in your data warehouse, creating a view called stg_customers with transformed customer data.

Using Common Table Expressions (CTEs)

CTEs make your models more readable and maintainable by breaking complex queries into logical building blocks. They’re a powerful way to structure your transformations:

-- models/marts/customer_orders.sql
WITH customers AS (
    SELECT * FROM {{ ref('stg_customers') }}
),

orders AS (
    SELECT * FROM {{ ref('stg_orders') }}
),

customer_orders AS (
    SELECT
        customer_id,
        COUNT(order_id) as order_count,
        SUM(amount) as total_spent
    FROM orders
    GROUP BY customer_id
)

SELECT
    customers.customer_id,
    customers.first_name,
    customers.last_name,
    customers.email,
    COALESCE(customer_orders.order_count, 0) as order_count,
    COALESCE(customer_orders.total_spent, 0) as total_spent
FROM customers
LEFT JOIN customer_orders USING (customer_id)

CTEs offer several benefits:

Improve readability by breaking complex logic into named sections
Allow you to reuse intermediate calculations
Make troubleshooting easier by separating transformation steps

Model Dependencies with ref()

The ref() function is one of dbt’s most powerful features. It allows you to reference other models, automatically creating dependencies:

-- models/marts/customer_lifetime_value.sql
WITH customer_orders AS (
    SELECT * FROM {{ ref('customer_orders') }}
)

SELECT
    customer_id,
    total_spent,
    total_spent * 0.15 as estimated_future_value,
    total_spent * 1.15 as lifetime_value
FROM customer_orders

When you use ref():

dbt automatically determines the correct schema and table name
dbt builds a dependency graph, ensuring models run in the correct order
dbt creates lineage documentation for your project

A key benefit is that if you rename models or change schemas, dbt handles all the references for you.

Model Configuration with config()

The config() function lets you control how a model is materialized and other settings:

-- models/marts/large_summary_table.sql
{{ 
  config(
    materialized='table',
    sort='date_day',
    dist='customer_id'
  ) 
}}

SELECT
  date_trunc('day', created_at) as date_day,
  customer_id,
  sum(amount) as total_amount
FROM {{ ref('stg_orders') }}
GROUP BY 1, 2

Common configuration options include:

materialized: How the model should be created (‘view’, ‘table’, ‘incremental’, ‘ephemeral’)
schema: Which schema the model should be created in
tags: Labels to organize and select models
Database-specific options (like sort, dist, cluster_by, etc.)

Using Jinja for Dynamic SQL

Jinja is a templating language that allows you to generate dynamic SQL. dbt uses Jinja to make your transformations more flexible and reusable.

Conditional Logic

Use if/else statements to adapt your SQL based on conditions:

SELECT
  order_id,
  order_date,
  {% if target.name == 'prod' %}
    amount
  {% else %}
    amount * 100 as amount_in_cents
  {% endif %}
FROM {{ ref('stg_orders') }}

Looping

Generate repetitive SQL using for loops:

SELECT
  order_id,
  {% for i in range(1, 5) %}
    item_{{ i }}_id,
    item_{{ i }}_quantity,
    {% if not loop.last %},{% endif %}
  {% endfor %}
FROM {{ ref('stg_order_items') }}

Variables

Use variables to make your models configurable:

-- Using a variable defined in dbt_project.yml or passed via --vars
SELECT *
FROM {{ ref('stg_orders') }}
WHERE order_date >= '{{ var("start_date", "2020-01-01") }}'

Model Organization Best Practices

Organize your models to reflect their purpose in your analytics pipeline:

Staging Models

Staging models clean and standardize source data:

Naming and datatype standardization
Simple filtering
One-to-one relationship with source tables
Typically materialized as views

-- models/staging/stg_customers.sql
SELECT
  id as customer_id,
  first_name,
  last_name,
  email,
  -- Convert to ISO date format
  PARSE_DATE('%Y-%m-%d', date_joined) as date_joined
FROM {{ source('jaffle_shop', 'customers') }}
WHERE id IS NOT NULL

Intermediate Models

Intermediate models combine and transform staging models:

Join related data sources
Apply business logic
Create reusable building blocks
Typically materialized as views

-- models/intermediate/int_customer_orders.sql
SELECT
  o.order_id,
  o.customer_id,
  c.email,
  o.order_date,
  o.status,
  o.amount
FROM {{ ref('stg_orders') }} o
JOIN {{ ref('stg_customers') }} c ON o.customer_id = c.customer_id

Mart Models

Mart models prepare data for business consumption:

Oriented around business entities (customers, products, etc.)
Optimized for specific use cases
Include calculated metrics
Often materialized as tables for performance

-- models/marts/finance/order_payment_summary.sql
{{
  config(
    materialized='table'
  )
}}

SELECT
  date_trunc('month', o.order_date) as order_month,
  p.payment_method,
  count(distinct o.order_id) as order_count,
  sum(o.amount) as total_amount
FROM {{ ref('int_customer_orders') }} o
JOIN {{ ref('stg_payments') }} p ON o.order_id = p.order_id
GROUP BY 1, 2

Troubleshooting Models

When your models have issues, use these strategies to troubleshoot:

Compiling Without Running

Use dbt compile to see the generated SQL without running it:

dbt compile --models customer_lifetime_value

Then check the compiled SQL in target/compiled/[project_name]/models/...

Execute Specific Models

Run only the model you’re working on:

dbt run --models staging.stg_customers

Or run a model and everything that depends on it:

dbt run --models +customer_lifetime_value

Check Logs

Detailed logs are available in the logs/ directory and often contain helpful error information.

Advanced Model Techniques

Custom Schemas

Generate custom schemas to separate models by team or environment:

{{ 
  config(
    schema='marketing_' ~ target.name
  ) 
}}

SELECT * FROM {{ ref('stg_marketing_campaigns') }}

Post-Hooks

Execute SQL after a model is created, such as granting permissions:

{{ 
  config(
    post_hook='GRANT SELECT ON {{ this }} TO ROLE analytics_readers'
  ) 
}}

SELECT * FROM {{ ref('stg_customers') }}

Documentation in Models

Add descriptions to your models using YAML files:

version: 2

models:
  - name: customer_orders
    description: "One record per customer with order summary data"
    columns:
      - name: customer_id
        description: "The primary key of the customers table"
      - name: order_count
        description: "Count of orders placed by this customer"
        tests:
          - not_null
      - name: total_spent
        description: "Total amount spent on all orders"

These descriptions will appear in your automatically generated documentation. By mastering models and transformations in dbt, you can build a reliable, maintainable analytics pipeline that transforms raw data into valuable business insights.

​What Are Models?

​Creating Your First Model

​Using Common Table Expressions (CTEs)

​Model Dependencies with ref()

​Model Configuration with config()

​Using Jinja for Dynamic SQL

​Conditional Logic

​Looping

​Variables

​Model Organization Best Practices

​Staging Models

​Intermediate Models

​Mart Models

​Troubleshooting Models

​Compiling Without Running

​Execute Specific Models

​Check Logs

​Advanced Model Techniques

​Custom Schemas

​Post-Hooks

​Documentation in Models