What is dbt and How to Use It?

In modern data engineering workflows, the transformation step of the ELT process has become increasingly critical. This is where dbt (data build tool) comes in. dbt is an open-source tool that enables data teams to transform data in their cloud data warehouses using just SQL and a bit of YAML.

Rather than relying on traditional ETL tools, dbt follows the ELT model:
Extract and Load are handled by tools like Fivetran or Airbyte, and Transform is performed directly inside the data warehouse by dbt.

What is dbt?

dbt is a command-line tool (also available as a cloud service) that allows data analysts and engineers to:

• Write transformation logic in SQL

• Organize code in modular files and folders

• Use Jinja templates to create dynamic SQL

• Test and validate data

• Automatically generate documentation

• Version control transformation logic with Git

The result is a code-first, testable, and maintainable transformation layer inside your data warehouse.

dbt supports popular warehouses like:

• Snowflake

• BigQuery

• Redshift

• Databricks

• Azure Synapse (via plugins)

How Does dbt Work?

At its core, dbt converts your SQL models into tables or views inside the data warehouse by running SELECT statements.

The workflow typically includes:

1. Create a dbt projectdbt init my_project

2. Define models

   Inside the /models folder, you write .sql files like:
   – models/active_users.sql
   SELECT *
   FROM users
   WHERE is_active = TRUE

3. Run models
   To build tables/views in your warehouse:

   dbt run

4. Add tests
   models:
   – name: users
   columns:
   – name: id
   tests:
   – unique
   – not_null,
5. Generate docs
   You can generate and view interactive documentation with:
   dbt docs generate
   dbt docs serve

   Benefits of Using dbt

   • Version-controlled data transformations (via Git)

   • SQL-centric, no need to learn complex new tools

   • Scalable and modular for teams

   • Easy testing and data validation

   • Automatically tracked dependencies between models

   Final Thoughts

   dbt brings software engineering best practices to analytics engineering. If your team works with a modern data warehouse and wants clean, testable, and maintainable SQL pipelines, dbt is one of the best tools you can adopt.

   Whether you’re a data analyst writing models or a data engineer designing pipelines, dbt helps bridge the gap between raw data and trusted business logic.