Why dbt is Essential for Modern Data Engineering

Why dbt is Essential for Modern Data Engineering

What is dbt?

dbt, short for data build tool, allows analysts and engineers to easily transform data within a database or data warehouse. Data transformation can be tedious, especially when multiple steps are involved. In analytics, it's common to have several steps from the data source to the reporting layer, with different transformations along the way. While plain SQL statements can handle these transformations, scheduling and maintaining workflows can be time-consuming and frustrating. Fortunately, dbt enables analysts to define multiple SQL models and combine them into a seamless workflow.

Data Transformations as Directed Acyclic Graphs (DAGs)

dbt is built around the concepts of models and DAGs (Directed Acyclic Graphs). A model is a single SQL script that produces one output (table or view). By stacking multiple models together using the ref() command, dbt creates a workflow. This workflow is structured as a DAG, meaning the graph flows in one direction without cycles. For example, consider three models: Model A (data cleaning), Model B (business logic), and Model C (reporting aggregation). In this case, Model C builds on Model B, and Model B builds on Model A, creating a linear data flow: A -> B -> C. This forms a valid DAG that dbt can easily process. A cycle like A -> B -> C -> B would be invalid due to the circular dependency.

SQL and Jinja

dbt leverages plain SQL combined with Jinja templating. Analysts can build templates and macros, making the templating capabilities incredibly powerful and significantly improving the productivity of the analytics team.

Do Not Repeat Yourself (DRY) Principle

dbt adheres to the DRY (Do Not Repeat Yourself) principle, encouraging analysts to avoid redundant code. The framework is designed to promote clean code, with business logic and reusable components encapsulated in macros, making it easier for other analysts to understand and maintain the codebase.


Ensuring data consistency is both crucial and complex. dbt allows you to write test scripts for each model and between different models. It comes with built-in testing capabilities, but you can also write custom tests tailored to your specific business needs.


In my opinion, dbt is a powerful tool that every analyst should consider. It integrates seamlessly with cloud data warehouses like BigQuery and Snowflake, as well as databases like DuckDB. The ability to test dbt code locally on a DuckDB database before deploying to a production-ready cloud data warehouse is particularly advantageous. Moreover, dbt offers numerous integrations with other data services, from ETL tools like dlthub to workflow tools like Dagster and Prefect.