Let’s set the scene. You are an analytics engineer at your company. You have several relational datasets flowing through your warehouse, and, of course, you can easily access and transform these tables through dbt. You’ve joined together the tables appropriately and have near-real time reporting on the relationships for each
entity_id as it currently exists.
But, at some point, your stakeholder wants to know how each entity is changing over time. Perhaps, it is important to understand the trend of a product throughout its lifetime. You need the history of each
entity_id across all of your datasets, because each related table is updated on its own timeline.
What is your first thought? Well, you’re a seasoned analytics engineer and you know the good people of dbt Labs have a solution for you. And then it hits you — the answer is snapshots!