Which of the following strategies can help reduce data skew in a distributed dataset in PySpark?

Using hash partitioning is an effective strategy to reduce data skew in a distributed dataset within PySpark. This technique works by distributing data across partitions in a way that minimizes the inequality among the sizes of partitions. When data is heavily skewed, certain partitions can become much larger than others, leading to performance bottlenecks as some tasks take significantly longer to complete compared to others. Hash partitioning addresses this issue by distributing the data based on a hash function, which ensures a more even spread of data across the available partitions.

The use of a hash function means that similar keys are likely to end up in different partitions, which helps balance the workload during processing and ensures that no single executor is overwhelmed with too much data. This ultimately improves the efficiency of the distributed processing and reduces the overall runtime of data transformations.

The other strategies provided do not directly tackle the issue of data skew. For instance, increasing executor memory may help with the resource allocation if tasks are running out of memory, but it doesn't address the inherent imbalance in data distribution. Similarly, while coalesce can optimize the number of partitions, it does not provide a means to redistribute data based on its content. Lastly, using DataFrames instead of RDDs may yield performance benefits due to optimization features