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Automated Anomaly Detection and Alerting

Use Case

Automated Anomaly Detection & Alerting

Automatically detect issues, improve alert accuracy and speed, and streamline troubleshooting processes

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The Challenge

Manual Detection, Alerts and Troubleshooting Processes

Relying on traditional observability tools to detect, alert on, and troubleshoot issues presents several shortcomings, including:

Slow Time to Identify: Massive data volumes must be centralized and analyzed before alerts are triggered.
Manual Detection: Teams manually created monitors and alerts. These are time-consuming and make it impossible to catch every issue.
Manual Troubleshooting Processes: When an alert fires, engineers must manually sift through logs to determine the root cause of the issue.

In addition to these shortcomings, teams frequently experience alert fatigue, leading to missed alerts or delayed responses.

The Solution

Automated Detection and Troubleshooting Insights

Edge Delta can help you optimize MTTI by alerting on issues faster and with greater accuracy. In addition, the platform provides insights to help you streamline your troubleshooting processes.

Automatically Detect Every Anomaly

Edge Delta automatically monitors your datasets for anomalous behaviors, so you can detect issues – whether you’ve built logic to detect them or not.
Increase Alert Speed and Accuracy

Trigger alerts at the infrastructure level to dramatically improve MTTI. Alerts fire when behavior meets Edge Delta’s confidence threshold, reducing noise and alert fatigue.
Simplify Troubleshooting Processes

When an alert fires, Edge Delta surfaces the relevant data, affected components, and other contextual information to make troubleshooting easy.

Detect Known and Unknown Issues

Edge Delta automatically detects anomalous behavior in your log data by:

Converting logs to metrics, baselining the KPIs, and notifying teams on irregular behavior
Detecting spikes in unusual ERROR, FAIL, or other negative sentiment logs
Assigning confidence of an anomaly against predefined thresholds to ensure accuracy