What does the term 'model' refer to in the context of dbt?

In the context of dbt, the term 'model' specifically refers to a SQL file that contains a query defining a transformation of the data. This is a fundamental concept in dbt, as transformations are the core function it performs within a data pipeline. When you create a model in dbt, you are essentially writing a SQL query that dictates how raw data should be transformed into a more refined format, which can be later used for analysis or reporting.

Once the models are defined and executed, dbt compiles them into tables or views in your data warehouse, which represent the transformed data. This allows teams to build and maintain their data transformations in a version-controlled manner, ensuring clarity and reproducibility.

The other options do not accurately describe what a model is in the context of dbt. Visualizations represent the presentation layer of data insights rather than the transformation logic itself. A data warehouse is the storage layer where transformed data resides but does not inherently define transformation processes. Lastly, a set of analytics functions refers to computations or operations performed on the data but does not encapsulate the concept of a transformation model.