If you are working with models that handle recorded event data or facts, you could fall into a situation where your models are completely refreshed at every dbt execution and most of the data was already in place in your tables. This may be because these types of data rarely change once created (e.g bank transactions).
Incremental dbt models work well in these scenarios and help speed up the dbt model creation and not overhead databases. Incremental models are materialized as tables, but they are not fully refreshed at every dbt execution. Only new records that were not previously in the table are added.
In this lab step, you will create two dbt incremental models by using two different strategies in order to let dbt understand whether a row needs to be added or not.
Upon completion of this lab, you will be able to:
To get the most from this lab, you should have basic knowledge of dbt. To get ready, you can use the following labs: