The realm of data modeling and transformation is increasingly intricate, with dependencies weaving a complex web among various models. As your dbt projects grow in complexity, handling these dependencies becomes an integral part of your workflow. In this article, we’ll outline how to manage dependencies in a complex dbt project, ensuring proper order of execution using resources and ref models.
Recognizing the Need for Dependency Management
Imagine a hypothetical scenario where your project freshers_in_university has multiple models like freshers_in_arts, freshers_in_science, freshers_in_commerce, and so on. Each of these models relies on one another to provide a comprehensive view of freshers in all university programs. If these models aren’t executed in the correct order or if a model fails midway, it can disrupt your entire project and lead to inaccurate data outcomes. This is where dependency management comes into play.
Using ref to Manage Dependencies
In dbt, dependencies among models are managed by using the ref function. This function refers to another model and creates a dependency. When dbt runs, it uses these ref calls to build a directed acyclic graph (DAG) of models and runs them in the correct order.
Consider a scenario where the model freshers_in_arts depends on two base models: freshers_in_university and freshers_in_specific_courses. In your freshers_in_arts.sql file, you would use the ref function to establish these dependencies:
WITH arts_freshers AS (
SELECT *
FROM {{ ref('freshers_in_university') }}
WHERE program = 'Arts'
),
specific_courses AS (
SELECT *
FROM {{ ref('freshers_in_specific_courses') }}
WHERE course_type = 'Arts'
)
SELECT
arts_freshers.*,
specific_courses.course_name
FROM arts_freshers
JOIN specific_courses ON arts_freshers.course_id = specific_courses.course_id;In this example, dbt will ensure that the models freshers_in_university and freshers_in_specific_courses are built before freshers_in_arts.
Dbt Resources for Dependency Management
Dbt provides several resources to manage and visualize dependencies, the most significant of which is the dbt documentation website. This site provides a graphical representation of your project’s DAG. You can use it to identify dependencies, track data lineage, and debug issues.
Another useful resource is the dbt run command-line tool. This tool takes into account the dependencies among your models and executes them in the correct order. For example, if model B depends on model A, dbt run will build model A before model B.
Ensuring the Proper Order of Execution
The order of execution is crucial in a dbt project. If models are not run in the proper sequence, it could lead to errors or inaccurate results. Thankfully, dbt handles this by using the dependencies defined by ref to determine the execution order. It builds models in an order that respects these dependencies, ensuring that each model has all the data it needs to execute successfully.
By utilizing the ref function and dbt’s built-in resources, you can effectively manage dependencies, ensuring the correct order of execution and preventing potential data inaccuracies. As your project grows, remember that efficient dependency management is a continuous process that requires regular evaluation and adjustments.
