This course explores Jinja templates and macros in dbt, enabling you to automate SQL generation, enhance reusability, and optimize query logic. You’ll learn to create dynamic SQL, implement macros, and use control structures to streamline data workflows. By the end, you’ll be equipped to write flexible, efficient, and maintainable dbt models.

Jinja Templates

Jinja is one of the strengths of dbt. This templating language allows us to do a whole bunch of things that would otherwise be impossible with plain SQL. Thanks to it, we can notably factorize our code and thus apply the DRY (Don’t Repeat Yourself) principle.

dbt allows dynamic SQL generation using Jinja, and it can be applied to any SQL within a dbt project, including models, analyses, tests, and hooks.

It enhances SQL in dbt by enabling advanced functionalities like

• Control structures (if statements, loops)
• Creating reusable macros, similar to functions in programming languages, to simplify and automate SQL code
• Environment variables for deployments
• Dynamic project builds
• Query-based operations

Using Variables in SQL

In the example below, instead of hardcoding the value for start_date, we use Jinja syntax to dynamically insert the value passed via the --vars flag. If no value is provided, the default value (“2024-01-01”) will be used.

SELECT *
FROM customers
WHERE signup_date >= '{{ var("start_date", "2024-01-01") }}'

This approach makes the query more flexible and reusable by allowing you to pass different values for start_date without modifying the SQL code.