Davis® Anomaly Detection app

Latest Dynatrace

The Davis® Anomaly Detection app provides you with a unified overview of all anomaly detection configurations in your Dynatrace environment.

Get an overview of anomaly detection configurations

In Dynatrace, go to Davis® Anomaly Detection.

At a glance, you see various information on your existing anomaly detection configurations, such as their status, source, the type of anomaly prediction model, and more—select Columns to view and configure available columns. You can also filter the table by any of these parameters.

An important parameter to look at is the Status of the configuration. If there's an error, the status is displayed as Error—select it to open the detailed report in a notebook.

Create an anomaly detector

To manually create an Anomaly Detection configuration

Go to Davis® Anomaly Detection .
Select Anomaly Detector > Create your own Anomaly Detector.
Give your configuration a meaningful Title.
Expand Configure your query and provide the DQL query to fetch your data.
Use the interval: 1m parameter to ensure proper data resolution for the analysis.
Expand Customize parameters and define the analyzer and its parameters. For details, see Analyzer type and parameters.
Expand Create an event template and configure the event triggered by the configuration. For details, see Event template.
Select Create.
Check the Status of the new configuration shortly after creation to ensure there are no errors in the execution.

Create an anomaly detector in Notebooks

With Davis for Notebooks, you can preview your Anomaly Detection configuration and evaluate its effectiveness. This option takes you to the Notebooks app, where you configure the query and monitoring strategy, and then back to the Davis® Anomaly Detection app to create an event template.

Go to Davis® Anomaly Detection .
Select Anomaly Detector > Anomaly Detector in Notebooks.
Select a notebook in which you want to preview your configuration.
This action takes you to Notebooks.
Add a new Query Grail or Metrics section and query the data you're interested in.
For a DQL query, use the interval: 1m parameter to ensure proper data resolution for the analysis.
Select Options > Davis AI.
Activate the analyzer.
Select the required analyzer and configure it. For details, see Anomaly detection configuration.
Select Run analysis.
Once you're satisfied with the result, select > Open with and select Davis Anomaly Detection.
This action takes you back to Davis® Anomaly Detection.
Expand Create an event template and configure the event triggered by the configuration. For details, see Event template.
Select Create.
Check the Status of the new configuration shortly after creation to ensure there are no errors in the execution.

Transform a metric event configuration

Metric events enhance anomaly detection, expanding them beyond out-of-box use cases into metric-based events. With the power of DQL, you can extend this reach further.

To convert a metric event to the Anomaly Detection configuration

Go to Davis® Anomaly Detection .
Select Anomaly Detector > Improve metric selector configuration with DQL.
Select the required metric event and select Transform.
For auto-adaptive threshold and seasonal baseline, adapt the query to the 1-minute resolution.
1. For the newly created configuration, select > Edit.
2. Expand the Configure your query section.
3. Append the interval:1m parameter to the query.
4. Save your changes.
The converted metric event is automatically disabled, and the newly created configuration is active instead.
Check the Status of the new configuration shortly after creation to ensure there are no errors in the execution.