Spark handles data partitioning and distribution through its RDD (Resilient Distributed Dataset) abstraction. RDDs are divided into partitions, where each partition represents a subset of data. Spark automatically handles the distribution of partitions across the cluster, ensuring that data is processed in parallel across nodes. This enables Spark to perform tasks concurrently, leading to faster and more efficient data processing. Additionally, Spark allows users to manually control the partitioning of RDDs using partitioning functions, where they can define custom logic for distributing data based on specific criteria.

Spark uses a technique called data partitioning to divide data into chunks called partitions. Each partition is processed independently on different nodes of a cluster. Spark provides two types of partitioning: hash partitioning and range partitioning. Hash partitioning distributes data uniformly across nodes based on a hash function, while range partitioning divides data based on a specific range of values. By distributing data across the cluster, Spark achieves parallel processing and faster execution of tasks.