In addition to the aforementioned practices, another key aspect to consider is the choice of Spark data structures. Utilizing appropriate data structures such as DataFrames or Datasets instead of RDDs can lead to significant performance improvements due to their optimization techniques like Catalyst optimizer and Tungsten execution engine.

The best practices for optimizing Apache Spark performance include partitioning data properly, leveraging data locality, caching intermediate data, and using efficient transformations and actions. Additionally, tuning the Spark configuration, combining small tasks into larger ones, and using appropriate hardware resources can also boost performance.

One alternate solution to optimize Spark performance is to utilize advanced techniques like data skew handling and dynamic resource allocation. Data skew handling deals with skewed data distributions by applying techniques like salting or using specialized join algorithms. Dynamic resource allocation adjusts the allocated resources based on the workload, ensuring optimal resource utilization.