Is dbt becoming a mandatory tool for modern Data Engineering and transformation?

All answers to this question.

Absolutely, dbt has revolutionized the "T" in ELT for Data Engineering. The main advantage isn't just the SQL; it's the software engineering best practices it brings to data, like version control, modularity, and testing. Instead of messy stored procedures, you write modular models that dbt compiles. In my team, we moved from one giant 2000-line SQL script to small, testable models. This allowed us to use Git for code reviews and automated documentation generation, which made it much easier for new engineers to understand our logic and contribute to the data stack safely.

   Answered 2025-10-15 by Deborah Lewis

Does dbt integrate well with orchestration tools like Airflow that are commonly used in a complex Data Engineering stack?

   Answered 2025-10-18 by Peter Clark

• Yes, Peter, it integrates beautifully. Most teams use the BashOperator or specific dbt operators in Airflow to trigger "dbt run" and "dbt test" as part of their larger DAG. This allows you to treat your transformations as a first-class citizen in your orchestration layer, ensuring that data is only moved if the previous transformation and tests passed successfully.

     Commented 2025-10-25 by Gary Wright

The built-in testing features in dbt for Data Engineering allow us to ensure primary keys are unique and columns aren't null automatically.

   Answered 2025-11-02 by Frank Robinson

• The automated testing is a life-saver! It catches data quality issues at the source before they ever reach the final executive reports.

     Commented 2025-11-09 by Charles King