What does the term "data sharding" refer to?

The term "data sharding" specifically refers to a database architecture pattern that partitions data into smaller pieces, known as shards. This process involves dividing a large database into smaller, more manageable segments that can be distributed across multiple servers or nodes. As a result, each shard contains a subset of the total dataset, allowing for more efficient data retrieval, processing, and overall performance. This approach is particularly useful in large-scale applications where a single database may become a bottleneck due to high read and write loads.

By using sharding, organizations can improve scalability and enhance the reliability of database operations since each shard operates independently. If one shard experiences issues, it does not affect the others, which helps maintain the availability of the overall system. This is a critical strategy in cloud computing and distributed database systems, where high availability and responsiveness are essential.

The other options touch on related concepts but do not accurately define data sharding. For instance, while encryption enhances security, it is distinct from the partitioning of data that sharding entails. Similarly, combining data sources refers to data integration, which differs greatly from sharding. Lastly, while increasing redundancy is important for data durability, it does not capture the essence of data sharding's focus on partitioning for performance