In data management, what does "sharding" primarily refer to?

Sharding primarily refers to the practice of distributing data across multiple servers or nodes to improve performance and scalability. By dividing a large dataset into smaller, more manageable pieces called shards, applications can more efficiently query and manage the data. Each server holds a portion of the complete dataset, allowing for parallel processing and reduced load on any single server.

This approach helps in enhancing response times and overall system performance, especially as the volume of data and number of users increases. Shards can be distributed based on various criteria, such as user IDs or geographic locations, facilitating efficient data retrieval and minimizing bottlenecks.

The other choices involve important data management concepts but do not specifically describe sharding. While partitioning for redundancy is vital for fault tolerance, creating backups focuses on data preservation, and data encryption pertains to securing information—none of these directly relate to the core idea of distributing data across different servers to optimize performance and scalability.