Tuning Spark jobs to minimize execution time and resource usage. Handling out-of-memory errors and optimizing memory allocation. Addressing issues where a disproportionate amount of work is done by a small number of tasks. Ensuring Spark applications can recover gracefully from worker failures. Efficiently distributing resources among various applications running in a cluster. Managing the lifecycle of containers that execute application tasks.  ... View more