Database sharding is a method of horizontally scaling databases by dividing a large dataset into smaller, manageable pieces called shards, each stored on a different server. This technique helps distribute data and workload across multiple database nodes, enhancing the system's ability to handle large volumes and high traffic. Sharding is often associated with horizontal partitioning, where data is split by rows rather than columns, allowing for improved scalability, performance, and fault tolerance. Different sharding strategies, such as range-based, hash-based, and directory-based sharding, offer varied advantages and challenges, including issues with data distribution, operational complexity, and maintaining data consistency across shards. While sharding can be complex to implement, database platforms like Aerospike offer built-in sharding capabilities that automate data partitioning and distribution, ensuring high availability and low latency. Sharding is an effective complement to other scaling methods such as vertical scaling and replication, each addressing different aspects of database performance, capacity, and reliability.