Which solution will improve the data loading performance for a sales data dashboard using Amazon Redshift?

The selected solution, which involves splitting large .csv files and using a COPY command to load data into Amazon Redshift, significantly enhances data loading performance due to several key factors inherent to Redshift's design and optimization strategies.

Firstly, the COPY command in Amazon Redshift is specifically designed for bulk loading of data. It processes data in parallel, which means it can handle multiple files simultaneously, leading to much faster ingestion compared to using an INSERT statement. The parallel processing capabilities of Redshift leverage its architecture, allowing it to effectively distribute the workload across multiple nodes.

Secondly, splitting large .csv files into smaller sizes optimizes the loading process further. Smaller files are easier to process and can also improve the data distribution across slices in a cluster. This distribution is crucial for performance because when data is spread evenly, it reduces the chances of bottlenecks and maximizes the use of available resources. As a result, loading split files can decrease the time to ingest large volumes of data significantly.

Moreover, using the COPY command supports various optimizations such as automatic compression and support for file formats that can optimize storage and query performance. This means that data can not only be loaded quickly but also be stored efficiently, which contributes to better overall database performance for analytics workloads,