Anomaly Detection

Now that we know how to perform powerful queries on our log data, let's explore how Dynatrace detects anomalies by analyzing raw log records and converting them into metrics using OpenPipeline. Dynatrace enhances observability by transforming log data into time-series metrics, enabling anomaly detection through static thresholds, auto-adaptive baselines, and seasonal baselines. Static thresholds provide fixed limits for alerting, while auto-adaptive baselines learn and adjust to dynamic system behavior, and seasonal baselines account for recurring patterns such as daily or weekly cycles. This approach allows for proactive identification of performance issues and unusual behavior across your environment.

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Return to the Notebook titled Workshop - Workshop Exercises. We will be completing the Log Anomaly Detection linked Notebooks.

Log Anomaly Baselines

Complete the exercises found in the Notebook Workshop - Log Anomaly Baselines - Exercises.

Reference the Notebook Workshop - Log Anomaly Baselines - Answer Key as needed or upon completion.

Davis Anomaly Detection - Logs

Complete the exercises found in the Notebook Workshop - Davis Anomaly Detection - Logs.

Log Metric Anomaly Baselines

Complete the exercises found in the Notebook Workshop - Metric Anomaly Baselines - Exercises.

Reference the Notebook Workshop - Metric Anomaly Baselines - Answer Key as needed or upon completion.

Davis Anomaly Detection - Metrics

Complete the exercises found in the Notebook Workshop - Davis Anomaly Detection - Metrics.

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In the next section, we'll cover the primary features of building dashboards in Dynatrace.

• Continue to Dashboards in Dynatrace