How dbt works (SQL + Jinja + YAML) - Try It Yourself

How dbt works (SQL + Jinja + YAML)

📖 Scenario: You are working as a data analyst in a company that uses dbt (data build tool) to transform raw data into clean, usable tables for reporting. You want to understand how dbt combines SQL, Jinja templating, and YAML configuration to build data models step-by-step.

🎯 Goal: Build a simple dbt model using SQL with Jinja templating and configure it with YAML. You will create a source table, add a configuration variable, write a SQL model using Jinja, and finally display the compiled SQL output.

📋 What You'll Learn

Create a source table dictionary in YAML format

Add a configuration variable for filtering data

Write a SQL model using Jinja templating to filter data based on the config variable

Print the final compiled SQL query as output

💡 Why This Matters

🌍 Real World

dbt is widely used in data teams to manage and transform data in a clear, version-controlled way. Understanding how SQL, Jinja, and YAML work together helps you build reliable data models.

💼 Career

Data analysts and engineers use dbt to create clean datasets for reporting and analysis. Knowing how to write dbt models and configurations is a valuable skill in modern data roles.

1

Create a source table configuration in YAML

Create a YAML string variable called source_yaml that defines a source named raw_data with a table named users. The table should have columns id, name, and age.

dbt

# Create the YAML string variable called source_yaml
# Your code here

Need a hint?

Use triple quotes to create a multi-line YAML string. Follow the indentation exactly as shown.

2

Add a configuration variable for filtering age

Create a variable called min_age and set it to 18. This will be used to filter users who are at least 18 years old.

dbt

source_yaml = """
sources:
  - name: raw_data
    tables:
      - name: users
        columns:
          - name: id
          - name: name
          - name: age
"""

# Create the variable min_age and set it to 18
# Your code here

Need a hint?

Just assign the number 18 to the variable named min_age.

3

Write a SQL model using Jinja templating to filter users by age

Create a string variable called model_sql that contains a SQL query using Jinja templating. The query should select id, name, and age from {{ source('raw_data', 'users') }} where age >= {{ min_age }}.

dbt

source_yaml = """
sources:
  - name: raw_data
    tables:
      - name: users
        columns:
          - name: id
          - name: name
          - name: age
"""

min_age = 18

# Create the model_sql string with Jinja templated SQL
# Your code here

Need a hint?

Use triple quotes for the SQL string. Use Jinja syntax with double curly braces for source and min_age.

4

Print the compiled SQL query

Write a print statement to display the model_sql string.

dbt

source_yaml = """
sources:
  - name: raw_data
    tables:
      - name: users
        columns:
          - name: id
          - name: name
          - name: age
"""

min_age = 18

model_sql = """
select id, name, age
from {{ source('raw_data', 'users') }}
where age >= {{ min_age }}
"""

# Print the model_sql string
# Your code here

Need a hint?

Use print(model_sql) to display the SQL string.