Spark is popular for its ease-of-use, speed, and power in large-scale distributed data processing. However, it can face operational challenges due to misuse by users. Common issues include data skew, executor misconfiguration, join/shuffle operations, and memory problems. To address these issues, developers should ensure proper data partitioning, configure the right number of executors based on workload and data spread, optimize shuffle operations, and manage memory usage effectively. By addressing these common issues, Spark performance can be improved, and operational tasks can be made more efficient.