1. Data Storage and Retrieval: DB is primarily focused on storing and retrieving data, providing functionalities for creating, updating, and querying databases. On the other hand, dbt does not store data directly but instead relies on the underlying database to perform these operations. It is used for transforming and modeling data to create analytics-ready tables.

2. Data Transformation: DB typically offers limited capabilities for data transformation. Users need to manually write SQL queries or use specific programming languages to transform data within the database. In contrast, dbt supports more advanced data transformation features, such as data modeling, schema creation, and automated workflows, allowing analysts and data engineers to easily transform and reshape data without complex coding.

3. Dependency Management: In database systems, dependencies between tables and views need to be managed manually, often leading to challenges in maintaining data integrity and consistency. Dbt, on the other hand, provides built-in dependency management features. It allows users to define and maintain relationships between models, automatically organizing the order of execution based on dependencies, ensuring consistent and accurate outputs.

4. Version Control: When working with DB, version control of database objects, such as tables, views, and stored procedures, can become challenging. Changes made to the database schema often require careful coordination and documentation. Dbt, however, integrates seamlessly with version control systems like Git. It uses the concept of "versioned models" to manage schema changes, enabling easy collaboration and tracking of changes over time.

5. Data Testing and Documentation: Traditional DB systems lack built-in features for data testing and documentation. With dbt, users can write tests to validate the quality and accuracy of their transformed data. Dbt also generates documentation automatically, making it easier for data analysts and other stakeholders to understand and use the transformed data.

6. Continuous Integration/Continuous Deployment (CI/CD): In the realm of DB, implementing CI/CD pipelines for database changes often requires custom solutions and manual coordination. Dbt, on the other hand, provides native support for CI/CD by allowing users to define incremental models and managing the deployment of these models through automated workflows, enabling efficient and reliable deployment of data transformations.

In summary, while DB focuses on data storage and retrieval, dbt is specifically designed for data transformation and modeling. Dbt provides advanced capabilities for dependency management, version control, data testing, documentation, and CI/CD, which are not typically found in traditional database systems.