Which technique involves distributing data across multiple servers to enhance performance?

Sharding is the technique that involves distributing data across multiple servers to enhance performance. This method divides a large dataset into smaller, more manageable pieces known as "shards," allowing each shard to be stored on a different server. As a result, each server handles a fraction of the total workload, improving speed and efficiency. Sharding is particularly beneficial for applications with high volumes of traffic or data, as it enables parallel processing and reduces the likelihood of bottlenecks that can occur when a single server is tasked with handling all data requests or storage.

This distribution of data helps achieve greater scalability, as you can easily add more servers to accommodate growing datasets or increased user demands without compromising performance. Sharding also improves data access speeds since queries can be directed to the specific server containing the relevant shard, minimizing the time taken to retrieve information.