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

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

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

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

Hint

Use print(model_sql) to display the SQL string.

Practice

1. What is the main role of Jinja in dbt projects?

easy

A. To add logic and dynamic behavior to SQL queries

B. To write raw SQL queries without any modification

C. To manage configuration and documentation files

D. To execute the SQL queries on the database

Solution

1. Step 1: Understand Jinja's purpose in dbt

   Jinja is a templating language that allows adding logic like loops and conditions inside SQL files.

2. Step 2: Differentiate roles of SQL, Jinja, and YAML

   SQL writes queries, YAML manages configs/docs, and Jinja adds dynamic logic to SQL.

3. Final Answer:

   To add logic and dynamic behavior to SQL queries -> Option A

4. Quick Check:

   Jinja = logic in SQL [OK]

Hint: Jinja = logic inside SQL, YAML = configs/docs [OK]

Common Mistakes:

• Confusing Jinja with YAML for configs
• Thinking Jinja executes SQL queries
• Assuming Jinja writes raw SQL without changes

2. Which of the following is the correct way to use a Jinja variable inside a dbt SQL model?

easy

A. SELECT * FROM var('table_name')

B. SELECT * FROM {{ var('table_name') }}

C. SELECT * FROM {% var('table_name') %}

D. SELECT * FROM [[ var('table_name') ]]

Solution

1. Step 1: Recall Jinja syntax for variables

   Jinja variables are inserted using double curly braces {{ }} around expressions.

2. Step 2: Identify correct syntax for var function

   The correct syntax is {{ var('variable_name') }} to access a variable in dbt.

3. Final Answer:

   SELECT * FROM {{ var('table_name') }} -> Option B

4. Quick Check:

   Jinja variables use {{ }} [OK]

Hint: Use {{ var('name') }} to insert variables in SQL [OK]

Common Mistakes:

• Using single curly braces or wrong brackets
• Confusing Jinja tags {% %} with variable insertion {{ }}
• Using square brackets instead of curly braces

3. Given this dbt model SQL code, what will be the output SQL after rendering?

SELECT
  user_id,
  {% if var('include_email', false) %}
    email,
  {% endif %}
  created_at
FROM users

Assuming the variable include_email is set to true in dbt_project.yml.

medium

A. SELECT user_id, true, created_at FROM users

B. SELECT user_id, created_at FROM users

C. Syntax error due to misplaced Jinja

D. SELECT user_id, email, created_at FROM users

Solution

1. Step 1: Check the value of the variable include_email

   The variable include_email is true, so the if condition passes and the email column is included.

2. Step 2: Render the SQL with the if block included

   The SQL will have user_id, email, and created_at columns selected from users.

3. Final Answer:

   SELECT user_id, email, created_at FROM users -> Option D

4. Quick Check:

   include_email true means email included [OK]

Hint: If var true, include block inside {% if %} [OK]

Common Mistakes:

• Ignoring the variable value and excluding email
• Thinking Jinja syntax causes SQL errors
• Confusing variable default values

4. You wrote this YAML config in your dbt project:

models:
  my_project:
    +materialized: table
      users:
        +tags: ['important']

Why does dbt raise an error when running?

medium

A. Because the indentation for 'users' is incorrect under 'my_project'

B. Because '+materialized' cannot be set in YAML

C. Because tags must be a string, not a list

D. Because 'models' key is missing

Solution

1. Step 1: Check YAML indentation rules for dbt configs

   In dbt, model configs under a project must be indented properly; 'users' should be at the same level as '+materialized'.

2. Step 2: Identify the indentation error

   'users' is indented too far, making it a child of '+materialized' which is invalid.

3. Final Answer:

   Because the indentation for 'users' is incorrect under 'my_project' -> Option A

4. Quick Check:

   YAML indentation matters for nested configs [OK]

Hint: Check YAML indentation carefully for nested configs [OK]

Common Mistakes:

• Ignoring YAML indentation importance
• Thinking '+materialized' is invalid syntax
• Assuming tags cannot be lists

5. You want to create a dbt model that selects only active users from a table, but the 'active' flag is stored in a YAML config. Which approach correctly combines SQL, Jinja, and YAML to achieve this?

hard

A. Use Jinja to read YAML directly inside SQL without defining variables

B. Write WHERE active = true directly in SQL without YAML or Jinja

C. Define 'active_flag: true' in YAML, then use WHERE active = {{ var('active_flag') }} in SQL with Jinja

D. Set 'active_flag' in YAML but forget to use Jinja in SQL, so filter is missing

Solution

1. Step 1: Store the filter value in YAML as a variable

   Define 'active_flag: true' in YAML under vars or config to make it accessible.

2. Step 2: Use Jinja to insert the variable in SQL WHERE clause

   Use WHERE active = {{ var('active_flag') }} so the SQL filters active users dynamically.

3. Final Answer:

   Define 'active_flag: true' in YAML, then use WHERE active = {{ var('active_flag') }} in SQL with Jinja -> Option C

4. Quick Check:

   YAML vars + Jinja in SQL = dynamic filters [OK]

Hint: Use YAML vars + Jinja {{ var() }} in SQL WHERE [OK]

Common Mistakes:

• Hardcoding filter in SQL ignoring YAML
• Not using Jinja to insert YAML vars
• Trying to read YAML directly in SQL without var()