Which method can be utilized for incremental data loads?

The best method for conducting incremental data loads is Delta Lake with change tracking. This approach enables efficient data operations by only processing the changes made since the last load, rather than reloading the entire dataset. Change tracking allows for the identification of inserts, updates, and deletions at a granular level.

Delta Lake ensures data consistency with ACID (Atomicity, Consistency, Isolation, Durability) transactions, which is crucial for data reliability when performing incremental updates. Additionally, it supports features like time travel, which helps understand the state of the data at various points in time, making it easier to manage and track incremental changes.

In contrast, using full data refreshes would entail reloading the entire dataset every time data is updated, which is often inefficient, especially for large datasets. In-memory data processing might optimize data access during compute operations but does not inherently facilitate incremental loading. Static data ingests refer to loading data as a one-time batch process, which also does not support the continuous and efficient updating of data that incremental loading requires.