How is data aggregated in Datadog?

Data aggregation in Datadog is primarily performed by utilizing time intervals and tags. This process allows users to organize and summarize their data effectively. Time intervals help define the periods over which data points are grouped, enabling users to analyze trends and understand patterns over specific timeframes. Tags, on the other hand, provide a way to categorize and filter data based on various attributes (such as host, environment, service, etc.), making it easier to extract meaningful insights from the collected metrics.

By combining these two methods, Datadog can efficiently aggregate large datasets, allowing users to monitor their applications and infrastructure more effectively, and ultimately drive better decision-making based on the visualizations and metrics produced. This makes the aggregation method well-suited for dynamic environments where data points can vary widely in terms of usage and relevance.