In addition to tuning garbage collection settings and using efficient serialization formats, you can also consider leveraging Spark's memory management features. For example, you can explicitly set the amount of memory allocated to Spark using the `spark.memory.fraction` configuration property. Another optimization technique is to use columnar storage formats like Parquet or ORC, which can compress data and reduce memory usage. Lastly, you can use techniques like data filtering and aggregation to reduce the amount of data processed and stored in memory.

One strategy to optimize memory usage in Spark is to tune the garbage collection settings. By adjusting parameters such as the heap size and the garbage collection algorithm, you can improve memory efficiency and reduce overhead. Another approach is to make use of data serialization formats like Apache Arrow or Kryo, which can help reduce memory footprint. Additionally, you can leverage Spark's data partitioning and caching capabilities to minimize data movement and improve memory utilization.

Optimizing memory usage in Spark involves careful consideration of various factors. In addition to tuning garbage collection settings and using optimized serialization formats, you can explore other options like off-heap memory storage, which allows you to store data outside the Java heap, reducing garbage collection overhead. Another technique is to enable memory compression, which can reduce the size of data in memory. Furthermore, you can utilize Spark's dynamic allocation feature to allocate and release memory based on the workload, ensuring efficient memory utilization.