dbt has significantly transformed the management of ETL processes by introducing software engineering best practices, such as version control, code reviews, and CI/CD pipelines, into data transformation workflows. By treating ETL like software development, dbt enhances both the scalability and efficiency of data operations, enabling faster and more reliable data processing. It promotes the use of modular, reusable SQL code that aligns with modern ETL pipelines, optimizing performance and cost management across cloud data warehouses. dbt facilitates agile, test-driven approaches to data modeling, allowing data analysts and engineers to focus more on strategic activities rather than routine data handling. Its features, such as unit testing for data models, dependency and data lineage management, and integration with version control systems, streamline ETL workflows and ensure data integrity, making dbt a pivotal tool in modern analytics engineering.