To tackle the complexities of event management, it’s crucial to understand how data evolves throughout the process and its role in managing and correlating events.
To begin, we collect data from a multitude of sources, a task Elastic is adept at handling with its robust capabilities for integrating new data streams. Ideally, there should be some commonalities between these data sets — for instance, how one event, such as too high CPU usage, is also relevant in another data set, perhaps through delayed response times. This overlap allows us to establish a causal relationship — high CPU usage may be the culprit behind slowed application performance.
Moving on, we leverage Elastic to transform this granular monitoring data into actionable alerts, such as notifications for anomalously long response times for a specific application or excessive CPU loading in particular containers or virtual machines. Initially, each alert operates in isolation.
The next phase in event management and correlation is providing additional context to these isolated alerts, seeking to uncover any interconnections among them. The goal is to enable the system to group alerts likely stemming from a similar source. This crucial task is handled at the event layer, where we employ Elastic Case Management to combine related alerts into a single case, illuminating the potential correlations.
Lastly, at the incident layer, we define what qualifies an event to be escalated to an incident status. This involves considering various factors, such as scheduled maintenance periods. For example, if an alert corresponds to a period when system downtime is planned, we may disregard the event. This discernment ensures the efficient allocation of resources to the events that truly warrant attention.
Leave a Reply