What is the concept of incremental modeling in dbt?

Incremental modeling in dbt refers to the strategy of only processing new or changed data rather than reprocessing historical data each time the transformation runs. This approach significantly improves performance and efficiency, especially with large datasets, because it minimizes the amount of data that needs to be processed during each run.

By focusing on just the new or modified records, it allows for faster updates and reduces computational overhead. This is particularly beneficial in data pipelines where data is continuously being added or updated, as it keeps the models current without the need to reprocess everything from scratch. Incremental models achieve this by leveraging features like unique identifiers to track changes, enabling the analytics engineer to maintain up-to-date tables without unnecessary data repetition.