What is dbt and what value does it bring to Data Engineering?

dbt (Data Build Tool) is an open-source framework focused on transforming data within the analytics engineering pipeline. Unlike traditional ETL tools that extract, transform and load data, dbt handles only the transformation step directly inside the data warehouse or data lake. The key advantage is that dbt provides SQL-based transformations in a structured, version-controlled and testable format. This allows teams to develop data models like software – with modularity, reusability and automated testing.

The value of dbt in data engineering is multifaceted:

1. Standardization
   dbt brings best practices such as “software engineering for data” into the BI domain.
2. Transparency
   All transformations are documented and versioned as code.
3. Automation
   dbt integrates into CI/CD pipelines and enables automated deployments.
4. Quality Assurance
   Built-in tests and documentation improve data quality.

dbt has evolved significantly in recent years and offers different variants and platforms:

1. dbt Core
   The open-source version, executed locally or in self-managed environments. It is ideal for developers who want full control over their infrastructure.
2. dbt Cloud
   A hosted platform that extends dbt Core with additional features such as a web UI, job scheduling, API access and user management.
3. dbt Cloud Enterprise
   For large organizations with advanced security and governance requirements.
4. dbt Cloud + dbt Fusion
   dbt Fusion is a concept that simplifies integration with other tools and platforms to create a seamless analytics engineering environment.

dbt is not a traditional ETL tool, but operates directly on the data warehouse. It supports a wide range of modern cloud data platforms and databases, including:

• Snowflake
• BigQuery
• Amazon Redshift
• Databricks
• PostgreSQL
• Azure Synapse Analytics
• Microsoft Fabric (planned)

Integration is achieved through adapters that connect dbt to the respective platform. This makes dbt flexible and suitable for almost any modern data stack architecture. dbt Core is the foundation of the entire dbt ecosystem. It is controlled via the command line and executes transformations defined as SQL statements organized into so-called models. These models can reference one another, forming a Directed Acyclic Graph (DAG) that represents dependencies between transformations.

dbt Core is installed via Python and includes folders for models, tests, macros and documentation. Transformations are defined in SQL and can use Jinja templates for dynamic logic. dbt provides built-in tests (e.g. for not-null or unique constraints) and automatically generates documentation.

The major advantage of dbt Core lies in its flexibility and openness: it is free, extensible and can be integrated into nearly any modern data architecture.