Database sharding partitions data across multiple servers to handle massive scale. This technique helps companies manage growing datasets without overwhelming a single machine. It's a core strategy for modern applications that need to process billions of requests.

The approach splits a large database into smaller, more manageable chunks called shards. Each shard lives on its own server, allowing systems to distribute the load. This is crucial for distributed systems where single points of failure are unacceptable.

Key considerations include choosing a sharding key and managing cross-shard queries. Companies must plan for data rebalancing as traffic shifts. The goal is maintaining performance while ensuring data remains accessible and consistent across the entire cluster.