Which characteristic describes data level integrations?

Data level integrations are primarily concerned with the processes of data extraction, transformation, and loading (ETL). This characteristic is crucial because it highlights how data is handled, processed, and moved between systems. In data level integrations, the focus is often on ensuring that the data is accurately and effectively transformed to meet the requirements of the target system.

The inclusion of extraction, transformation, and loading emphasizes that data may need to be sourced from multiple origins, modified to match the target schema or business rules, and ultimately loaded into a final destination for use. This is a fundamental aspect of data integration processes, especially when dealing with large datasets or varied data structures from disparate systems.

While real-time data transfer, synchronous processing, and user interface components may be relevant to certain integration strategies, they do not encapsulate the essence of what data level integrations primarily focus on—managing and manipulating data itself rather than just its transmission or presentation.