Which method can help optimize a source aggregation process?

Using filters to limit data volume is an effective method for optimizing a source aggregation process. By implementing filters, you can ensure that only relevant and necessary data is pulled in, significantly reducing the amount of information that needs to be processed during aggregation. This not only speeds up the processing time but also minimizes resource consumption, allowing for more efficient use of system capabilities.

Choosing to limit data volume through filters can lead to quicker aggregation cycles, leading to more timely insights and enhancements in overall system performance. It helps in focusing the aggregation process on the most pertinent information, thus avoiding unnecessary clutter and potential bottlenecks that can occur when handling excessive amounts of data.

In contrast, running all sources at once during business hours can create a higher load that may overwhelm system resources. Disabling all connectors temporarily disrupts the data flow and defeats the purpose of aggregation. Finally, aggregating without any restrictions increases the risk of overloading the system, leading to inefficient processing and delayed results.