What is the most efficient method for loading multiple files into an Amazon Redshift cluster?

The most efficient method for loading multiple files into an Amazon Redshift cluster is to use parallel COPY commands. This approach leverages Amazon Redshift's ability to load data concurrently from multiple files or sources, which can significantly reduce the time it takes to ingest large volumes of data.

When using the COPY command, Redshift can take advantage of its distributed architecture, allowing multiple data slices to process and load information simultaneously. This parallel processing is optimal for performance, especially when dealing with large datasets, as it maximizes throughput and minimizes load times. By using multiple COPY commands in parallel, rather than a single one, you can ensure that data ingestion is done in a way that fully utilizes the computational resources available in the Redshift cluster.

While a single COPY command can be used to load data, it is less efficient when loading multiple files because it does not take advantage of the parallelism that Redshift can offer. Loading via Amazon EMR might be efficient for specific use cases or transformations but is not inherently better for simply loading files into Redshift. Similarly, manually splitting files into smaller batches can introduce additional complexity and overhead, which could make the process slower and less efficient compared to leveraging Redshift’s capabilities through parallel COPY commands.