Implement the 'repartition' function to manually adjust the number of partitions based on the data size, which requires pre-processing knowledge of the data volume.

Use the 'coalesce' function to reduce the number of partitions after filtering operations, which may lead to underutilization of resources for larger datasets.

Apply the 'cache' function to store all intermediate results in memory, which could lead to increased costs due to high memory usage for large datasets.

Utilize the 'persist' function with different storage levels (MEMORY_AND_DISK) to store intermediate results, allowing Spark to dynamically manage data storage based on workload and available resources.