In dbt, what is a 'snapshot'?

A 'snapshot' in dbt is primarily a feature used to capture historical changes in the data over time. This functionality allows users to track changes in source data and maintain a history of those changes, which is essential for analytical purposes. Snapshots are particularly useful for ensuring data consistency and creating reports that reflect the evolution of data.

When a snapshot is created, it takes a record of the current state of a dataset and saves it. Subsequent snapshots will compare new data against the existing snapshot and will save new records only when changes are detected. This technique enables users to analyze how data has evolved, which can be invaluable for understanding trends, compliance reporting, and auditing.

Other choices point to different concepts that do not accurately define what a snapshot is in the context of dbt. For instance, while a static view of data or a tool for real-time data monitoring may have some similarities in terms of capturing data, they do not encompass the key element of maintaining historical context that a snapshot does. Additionally, a backup of the database relates to data recovery rather than the specific function of tracking historical changes.