Explain the difference between an executor and a driver in Apache Spark.

In Spark, the driver is the master node that orchestrates the entire job. It parses the user program, creates a directed acyclic graph (DAG) of stages, schedules tasks, and tracks progress. Executors are worker processes launched on cluster nodes; each executor runs multiple tasks in parallel. The driver sends serialized tasks to executors, which execute them on local data partitions. Executors store intermediate data in memory or disk (RDD partitions) and return results to the driver. Key differences: the driver manages job control and metadata, while executors perform the actual computation. The driver runs in a single JVM, whereas executors can be many, each with its own JVM. If the driver fails, the whole job fails; executor failures are handled by rescheduling tasks. Understanding this separation is crucial for tuning performance, memory usage, and fault tolerance in Spark applications.