Data Engineering Associate with Databricks Practice Exam

What does the term "incremental ETL" refer to?

1. Loading all historical data

2. Processing all data at once

3. Handling only new data since the last ingestion

4. Loading data based on a defined schema

The correct answer is: Handling only new data since the last ingestion

Incremental ETL is a data processing approach that focuses on updating a data warehouse or data lake with only the new or changed data since the most recent data extraction. This method contrasts with traditional ETL processes that might involve reloading the entire dataset at each interval. By handling only the new data since the last ingestion, incremental ETL minimizes the volume of data processed, reduces the time required for data loading, and optimizes resource consumption. This method is particularly beneficial for large datasets where frequent updates occur, ensuring that any changes are captured effectively without the overhead of processing all historical data again. Incremental ETL workflows can lead to increased efficiency and quicker availability of updated data for analytical purposes. In this context, options that imply loading all historical data or processing all data at once do not align with the incremental approach. Similarly, loading data based on a defined schema refers more to the structure of the data than to the incremental nature of the ETL process itself. Therefore, the choice that defines incremental ETL correctly focuses on the handling of only the new data since the last ingestion.