Aerospike offers mechanisms for efficiently processing large data sets in parallel by utilizing partitioning schemes that are collectively exhaustive and mutually exclusive. These schemes allow data to be split into partitions or sub-partitions, enabling multiple worker tasks to process them concurrently. Aerospike organizes records into 4096 partitions using a hash function, ensuring uniform distribution across cluster nodes, and supports queries over these partitions with pagination for efficient data retrieval. For platforms requiring more than 4096 concurrent tasks, data can be further divided into sub-partitions using a digest-modulo function, allowing for rapid evaluation without accessing storage devices directly. The article discusses different split assignment schemes, such as At-Most N, At-Least N, and Exactly N splits, each with specific API call requirements. A parallel query framework is provided to test these assignments, accommodating various parameters like the number of splits, workers, query types, and processing modes. The potential for high degrees of parallelism is highlighted, though it notes that extreme parallelism might not always yield benefits, especially in complex computations requiring data shuffling across nodes.