signal-radar

You can monitor fleet telemetry data for operational anomalies by running detector-based scans against existing data sources, which identifies issues like error spikes or outdated secrets and routes alerts to relevant teams.

Anomaly detection in fleet operations involves scanning telemetry data to identify unexpected patterns before they become critical. You need it to proactively catch issues like error rate spikes or expiring secrets.

Yes, alerting on fleet operational anomalies requires access to a Cosmos DB signals container to store identified signals, alongside access to existing telemetry data sources like Sentry or PostHog for detection.

To run a scan for telemetry anomalies, execute the scan command, which utilizes Python libraries to process telemetry data from connected sources, detects anomalies using multiple detectors, and reports high-severity signals.

Yes, you can use Sentry and PostHog as data sources for detecting unexpected error spikes. The monitoring system applies multiple detectors to watch existing fleet telemetry data from these platforms.

The best way to route high-severity signals is to use detector-based monitoring that identifies anomalies in telemetry data and automatically routes critical alerts to the relevant agent inboxes for investigation.