Spark Data Partitioning Explained

Data partitioning is the process of dividing a dataset into smaller partitions for parallel processing and distributed computing. In Spark, data partitioning is done within Resilient Distributed Datasets (RDDs), where Spark will determine the number of partitions based on the number of partitions from the data source. Partitioning data allows Spark jobs to be executed in parallel across multiple nodes in a cluster, improving job efficiency. By controlling data partitioning, one can optimize the performance and resource utilization of Spark jobs.