What does the term 'sharding' refer to in database management?

The term 'sharding' in database management specifically refers to the practice of dividing data among multiple servers. This process is essential for achieving horizontal scalability, which allows an application to handle larger amounts of traffic and data by distributing the workload across several database instances. By spreading data across different servers, sharding enhances performance, as individual servers handle smaller subsets of the overall dataset, leading to reduced load times and increased efficiency in query processing.

In applications where large quantities of data are expected, such as social media platforms or e-commerce sites, sharding becomes crucial. It allows a database system to maintain performance levels as the volume of data and user requests grow. By implementing sharding, organizations can not only optimize their resources but also ensure high availability and fault tolerance.

The other options highlight different aspects of database management that do not correspond to sharding. Combining data for analysis relates to data aggregation processes, encrypting sensitive information pertains to data security measures, and creating backups involves replication and data preservation strategies. While these are important practices in their own right, they do not capture the essence of sharding.