What adjustment will enhance the COPY process in Amazon Redshift when loading a large aggregated file?

Splitting files to match the number of slices in the cluster is an effective adjustment to enhance the COPY process in Amazon Redshift when loading large aggregated files. Amazon Redshift uses a distributed architecture where data is processed in parallel across several slices within the compute nodes. Each slice handles a portion of the data, leading to improved performance during data loading operations.

When the files to be loaded are split according to the number of slices, each slice can read its portion of the file simultaneously. This parallel processing means that the COPY command can efficiently utilize the resources available in the cluster, significantly reducing the time taken to load the data. Ensuring that each slice gets an equal share of the workload can lead to optimal loading performance.

Other strategies, such as running the COPY command in parallel or uploading files individually, can also aid in performance but may not utilize the cluster's resources as efficiently as splitting files to match the number of slices. Increasing the compression level of gzipped files could reduce file size but may not directly address enhancing the loading process's parallelism. Therefore, aligning file sizes with the architecture of the cluster is the most effective approach in this context.