DAG stands for Directed Acyclic Graph. In the context of Apache Spark, a DAG represents a sequence of computations performed on data. When Spark runs an application, it creates a DAG of tasks to be executed, with each node representing an RDD and each edge representing a transformation applied from one RDD to another. This model allows Spark to optimize the execution plan by rearranging computations and minimizing data shuffling. The DAGScheduler divides the graph into stages that can be executed in parallel, significantly optimizing the processing time.
