Which transformation is specifically used to load data into a data store or compute resource?

The transformation used to load data into a data store or compute resource is referred to as a Sink. In data engineering and data processing pipelines, a Sink represents the destination where data is ultimately sent or stored after processing. This transformation enables the final step in a data flow, facilitating the writing or loading of data into various storage solutions such as databases, data lakes, or other data services.

Sinks can involve different target formats and mechanisms depending on the requirements of the data pipeline, making them critical in ensuring data is accurately placed for further analysis or use. In contrast, other transformation types serve different purposes within the data flow; for instance, Window functions are used for analyzing data over specified ranges, and Derived Column transformations create new columns based on existing data but do not load data into storage. Understanding these roles clarifies how data moves from extraction and transformation stages into a finalized storage or computation resource.