Focus on partitioning based on the data's size, as this is the most critical factor for analytical workloads.

Implement a partition strategy based on the data's natural partition key, such as date or time, to improve query performance and reduce the amount of data scanned.

Use a hash-based partitioning method to distribute the data evenly across multiple partitions, regardless of the data's characteristics.

Do not implement any partition strategy, as it is not necessary for analytical workloads in Azure Data Lake Storage Gen2.