In dbt, what does a model represent?

In dbt, a model represents a SQL file that defines how data should be transformed. This is fundamental to dbt's functionality, as it allows analysts and engineers to create structured transformations on raw data stored within a data warehouse.

A model in dbt is written in SQL, and it typically contains the logic for transforming, filtering, or aggregating data from various sources into a structured format that is more useful for analytics and reporting. Each model corresponds to a table or view in the data warehouse after it is run through the dbt framework. This transformational logic is reusable, maintainable, and can be easily documented, aligning with dbt's philosophy of treating the data pipeline as code.

The other choices represent different concepts that do not accurately define a model: a snapshot of data at a certain time refers to data versioning rather than transformation; a script for generating reports suggests a focus on output rather than the transformation process; and a log of errors pertains to monitoring and debugging rather than defining data transformations.