What are common causes of resource limitations (memory) that could lead to this error?

Insufficient memory allocated to the Spark driver or executors is a frequent culprit. Check the `spark.driver.memory` and `spark.executor.memory` configurations. Large datasets, complex transformations, or poorly optimized code can lead to excessive memory usage. Monitor your Spark application's memory consumption using the Spark UI to identify bottlenecks. Consider increasing memory allocations if needed.