What is an 'exposure' in dbt?

In dbt, an 'exposure' serves as a way to document and showcase analytics use cases that depend on specific models, such as dashboards or reports. By defining exposures, analytics engineers can provide clear visibility into how data transformations are utilized in various business contexts. This creates a connection between the analytical output and its practical applications, which is crucial for understanding the value of the data models created.

Exposures offer an organized method for tracking the outputs generated from dbt models and connecting them to the specific analytical tools and insights that leverage these outputs. This documentation can significantly improve stakeholder understanding and collaboration as it clarifies which metrics and reports rely on particular transformations in the data model.

In contrast, other options such as a table for storing raw input data or a method for visualizing raw data sources do not capture the idea of linking analytics outcomes to their sources in the same structured manner. Additionally, a security measure to protect sensitive data is unrelated to the concept of an exposure since it focuses on data governance rather than the analytics context.