Data partitioning is a technique used in distributed systems and databases to divide a large dataset into smaller, more manageable parts, referred to as partitions. Each partition is independent and contains a subset of the overall data.

In data partitioning, the dataset is typically partitioned based on a certain criterion, such as data range, data size, or data type. Each partition is then assigned to a separate processing node, which can perform operations on its assigned data subset independently of the others.

Data partitioning can help improve the performance and scalability of large-scale data processing applications, as it allows processing to be distributed across multiple nodes, minimizing data transfer and reducing processing time. Secondly, by distributing the data across multiple nodes or servers, the workload can be balanced, and the system can handle more requests and process data more efficiently.

Key terminology and concepts

• Partition: A partition is a smaller, more manageable part of a larger dataset, created as a result of data partitioning.

• Partition key: The partition key is a data attribute used to determine how data is distributed across partitions. An effective partition key should provide an even distribution of data and support efficient query patterns.

• Shard: A shard is a term often used interchangeably with a partition, particularly in the context of horizontal partitioning (will be discussed later).