What is a characteristic of incremental pipelines?

Incremental pipelines are designed to efficiently process data by focusing only on new or changed information since the last execution. This characteristic allows them to minimize resource usage and improve performance, as they do not repeat the processing of data that has already been handled in prior runs.

This approach is particularly beneficial for scenarios involving large datasets, where processing all the data anew each time would be time-consuming and resource-intensive. Incremental pipelines rely on mechanisms such as timestamps, versioning, or change data capture to identify and process only the relevant changes. This design ensures that data processing workflows are streamlined, enabling faster and more efficient updates.

The other options may not accurately reflect the nature of incremental pipelines. For instance, while some pipelines can be built using Java APIs, it is not a defining characteristic of incremental pipelines. Additionally, processing all data every time is more typical of batch processing strategies rather than incremental ones. Lastly, the notion that incremental pipelines are not suitable for large amounts of data contradicts their purpose; they are often employed precisely to manage and optimize the processing of large datasets efficiently.