What is a valid way to enhance aggregation efficiency?

Enhancing aggregation efficiency is crucial for ensuring that identity data is processed rapidly and accurately, particularly when dealing with large datasets. Enabling parallel processing where supported significantly boosts efficiency by allowing multiple aggregation tasks to be executed simultaneously. This approach takes full advantage of system resources, reducing the overall time required to complete aggregations. Since aggregations can be resource-intensive, distributing the workload across various processors or threads can lead to a substantial decrease in processing time.

By contrast, implementing sequential processing only would slow down the aggregation process as tasks would have to wait for one another to complete before starting, leading to inefficiencies. Increasing the number of sources aggregated could demand more resources and processing time, which may not result in quicker completion if they cannot be handled in parallel. Running aggregations during peak hours may lead to resource contention with other critical operations, which can further hinder efficiency rather than enhance it. Therefore, enabling parallel processing is the most effective strategy to optimize aggregation performance.