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🎥 Introduction to Anomaly Detection based on DQL

Community Team
Community Team


Dynatrace Davis AI has been solving the challenge of anomaly detection on high cardinal data for many years and with that, reduces alert noise and avoids alert fatigue. As announced at Perform2024, Dynatrace brings the Davis AI Analyzer into Notebooks, Dashboards, and Workflows to leverage anomaly detection on ANY data that can be queried through DQL (Dynatrace Query Language).

To get a preview of the new extended capabilities, Andreas Grabner @andreas_grabner, DevSecOps Activist at Dynatrace invited David Bruendl @DavidBruendl , Product Manager at Dynatrace, to walk us through a full end-2-end scenario of leveraging the Davis AI Analyzer and configuring custom anomaly detection events. We will follow up with more use case-specific sessions in the coming weeks, but please familiarize yourself with the core capabilities.

Additional Links Discussed:
Dynatrace Anomaly detection doc: 
Dynatrace SaaS Trial: 
Dynatrace Playground: 

Chapter List:
00:00 - Introduction
02:23 - What you'll learn today
03:27 - The Scale of Cloud Observability
03:56 - The Challenge of anomaly detection in high cardinal data
05:50 - Reduce alert noise and avoid alert fatigue
09:49 - Davis AI supports you in every context
10:30 - Live Demo


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DynaMight Guru
DynaMight Guru

Will this solve this idea request?
Assuming the metrics are required to create metrics events.

Site Reliability Engineer @ Kyndryl

Hey @dannemca :-),

good question thanks. Well, the new Davis anomaly detector works on time series data (also |makeTimeseries). But in that use case,  you could set a workflow that produces a metric with a data point every minute.

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