Data aggregation is the process of collecting data and preparing it in a summarized form for further use.
A very popular example is statistical analysis, where raw data is aggregated over a period of time to provide statistics such as average, minimum, maximum, sum, and count. After the data is aggregated and written to a view or report, you can analyze the aggregated data to gain insights about specific resources or groups of resources.
Data aggregation can be applied at any size, from simple tables to data lakes, to summarize information and draw conclusions based on data-rich insights. What makes data valuable is the extraction of insights that point to important trends and outcomes and provide a better understanding of the information at hand. As a process of collecting data and presenting it in a summarized, report-based form, data aggregation helps achieve specific business goals or perform analysis at almost any scale.
And similarly, data aggregation can be used especially in IT alerting. For example, modern uptime platforms such as iLert use various forms of data aggregation to extract the necessary alerting information from event streams and thus avoid triggering an unnecessary flood of alerts in the event of a fault, but instead deliver the important notifications to the responder in a targeted manner.
Especially in times of distributed systems and the cloud, it often happens that monitoring systems fire events from all pipes when there is a failure. Through intelligent data aggregation, iLert ensures that out of these hundreds or even thousands of events, the information that is really important comes through as an alert and ensures that a first responder can do his job with a tailwind.
