Lots of people like to construct dashboards in Grafana, for monitoring and alerting – it’s fast, sleek, and practical. Zebrium is awesome for analytics in-part because we lay everything down into tables in a scale-out MPP relational column store at ingest. Each event type gets its own table with typed columns for the parameters; metrics data is also tabled; ditto for anomalies and incidents.
Automatically Spot Critical Incidents and Show Me Root Cause
That's what I wanted from a tool when I first heard of anomaly detection. I wanted it to do this based only on the logs and metrics it ingests, and alert me right away, with all this context baked in...
Automatically Spot Critical Incidents and Show Me Root Cause
At Zebrium, we have a saying: “Structure First”. We talk a lot about structuring because it allows us to do amazing things with log data. But most people don’t know what we mean when we say the word “structure”, or why it allows for amazing things like anomaly detection. This is a gentle and intuitive introduction to “structure” as we mean it.
As scale and complexity grow, there are diminishing returns from pre-deployment testing. A test writer cannot envision the combinatoric explosion of coincidences that yield calamity. We must accept that deploying into production is the only definitive test.
We structure machine data at scale
Zebrium helps dev and test engineers find hidden issues in tests that “pass”, find root-cause faster than ever, and validate builds with self-maintaining problem signatures. We ingest, structure, and auto-analyze machine data - logs, stats, and config - collected from test runs.
