What does 'sharding' mean in the context of data processing?

Sharding refers to the practice of breaking data into smaller, more manageable pieces, which can then be distributed across multiple servers or databases. This approach enhances the performance, scalability, and manageability of the data architecture. By dividing large datasets into smaller shards, a system can perform parallel processing, allowing for quicker data access and reducing the load on any single database or server. This technique is particularly useful in environments experiencing high volumes of transactions or queries, as it facilitates more efficient data retrieval and storage.

The other options focus on different aspects of data management but do not accurately describe sharding. Backed-up storage pertains to data redundancy safeguards, compressing data involves reducing file sizes for storage efficiency and speed, and archiving data refers to storing data that is no longer actively used. None of these concepts capture the essence of breaking data into smaller parts for enhanced processing efficiency, which is the core principle of sharding.