What defines a 'snapshot' in dbt?

A snapshot in dbt is best defined as a mechanism that allows users to capture historical changes in a source table. Essentially, snapshots are used to track changes to data over time, which is crucial for understanding data evolution and making timely decisions based on historical insights.

By implementing snapshots, analysts can create a versioned history of records in a table, enabling them to observe how data has changed, such as updates to customer information or status changes in a sales pipeline. This feature is particularly beneficial in scenarios where understanding the timeline of data revisions is necessary.

While temporary versions of tables, project backups, and metadata storage are important components of data management within dbt, they do not capture the essence of what a snapshot represents. Snapshots focus specifically on the temporal aspect of data, preserving a history that can be analyzed and queried at any point in time.