Data professionals and analysts are constantly searching for efficient ways to streamline their ETL/ELT processes. dbt, with its focus on transformation, modeling, and testing, has gained significant traction in the industry. On the other hand, DuckDB, a high-performance analytical database, has gained recognition for its speed and versatility.

In this session, we will examine use cases of deploying dbt and DuckDB to execute data transformations. We will analyze the strengths and limitations of this combination, considering factors such as data volume, complexity, and scalability. Through practical examples, we will evaluate whether the duo of dbt and DuckDB are indeed the real deal, or if they fall short of the hype.