Spark DAG: Execution Plan Explained

In Spark, a DAG (Directed Acyclic Graph) is a directed acyclic graph used to represent the execution plan of Spark jobs. Nodes in the DAG represent data transformation operations, while edges represent data dependencies. Each node represents an RDD (Resilient Distributed Dataset) operation, such as map, filter, reduce, etc. The DAG is built based on the dependencies of operations to ensure that each operation can only be executed after all its dependent operations are completed. The Spark engine optimizes and executes jobs based on the DAG to improve job performance and efficiency.