The primary purpose of data partitioning in a distributed computing environment is to distribute data across nodes. This approach divides a dataset into smaller partitions, allowing each node in the cluster to process its assigned partition independently. Such distribution facilitates parallel processing, optimizes resource utilization, and enhances the performance of distributed computations, as seen in systems like Apache Spark.