Log Management and Analytics use cases

The following use cases show just some of the ways you can use Log Management and Analytics to leverage your log data.

Create a log metric

In this use case, you need to count how many refused connections are recorded in your log data. For that, filter the correct logs and turn the number of occurrences into a log metric.

In this use case, you need to monitor an attribute of your logs, and you need to keep an eye on the error levels reported in your logs from your K8s cluster.

Create a log alert

In this use case, you need to set an alert based on the occurrence of log events. See how you can extract data from logs, create a processing rule, build an alert by forming a log event, and check if your alert captures logs that meet predefined criteria.

Resolve team dependencies

In this use case, you create a Log Analysis Dashboard that takes care of identifying bugs from logs, as well as grouping, triaging, and distributing to a bug tracker that clarifies ambiguous responsibilities and interdependencies.

Create anomaly detection metric

In this use case, you need to automate anomaly detection. See how you can extract data from logs, create a processing rule, create a metric, and create an alert that generates a notification if an anomaly occurs.

Real-time advanced observability with logs and DQL

In this use case, you want to observe mission-critical information over time found in your logs that are sent using log ingest API.

Troubleshoot with Logs for Kubernetes environments

In this use case, you monitor log data in a Kubernetes environment. Additionally, some of the log data might contain sensitive information that needs to be masked before sending it to Dynatrace.