Sharding and partitioning are both methods of breaking a large dataset into smaller subsets for improved scalability and performance. The key difference between the two is that sharding implies data distribution across multiple database instances, while partitioning does not. Partitioning involves dividing a large table into smaller parts to reduce query response time and improve maintenance. Horizontal partitioning, also known as sharding, divides data based on a shard key onto separate database servers or storage devices, spreading load and improving performance. The optimal partition size depends on dataset size, available storage capacity, and performance requirements. A good partition key should be chosen carefully to ensure even data distribution. Sharding can improve performance by reducing index size, distributing data over multiple machines, and segmenting data by geography. However, it introduces complexity and potential problems such as SQL complexity, additional software, single point of failure, fail-over server complexity, and backups complexity. Combining partitioning and sharding techniques is often required for data-intensive applications.