What are data partitions in Spark?

Partitioning data in Spark involves dividing data into multiple parts. This can increase the parallelism of Spark jobs, allowing multiple nodes in the Spark cluster to process different data partitions simultaneously, speeding up job execution. Data partitioning can be done using different strategies such as based on hash value, range, random, etc. By selecting an appropriate data partitioning strategy, the performance of Spark jobs can be effectively improved.