Operations | Monitoring | ITSM | DevOps | Cloud

There is observation then there’s analysis. Log Analytics falls under the latter category. Observation and analysis are not mutually exclusive; one builds upon the other. Similarly, Log analytics advances beyond simple log monitoring, enabling observability teams to identify trends and irregularities throughout your enterprise. To demystify what is Log Analytics, let’s first have a look at the definition.

Back in September when Cisco announced they were acquiring Splunk, we explained how the market was consolidating with Sumo Logic ahead of the pack, challenging traditional vendors with our cloud-native platform. Now that the deal is complete and Splunk is officially a Cisco company, we’re hearing from more Splunk customers who are considering their options.

It’s been nearly ten years since “The Ultimate CPU Alert – Reloaded” and its Linux version were shared with the SolarWinds community. At that time, managing CPU data from 11,000 nodes, with updates every five minutes to a central MSSQL database, was a significant challenge. The goal was to develop alerting logic to identify when a server was experiencing high CPU usage accurately.

Telemetry data is growing exponentially, but the business value isn’t increasing at a similar pace. Getting the right telemetry data is hard, so I recently had a conversation with Matt Aslett, Director of Research at Ventana Research, now a part of ISG, about five myths and realities in telemetry data handling.

Turning logs into metrics isn’t a new concept. A version of this functionality is implemented in most agents, visualization tools, and backends. It’s everywhere because converting logs to metrics has many practical applications and is one of the fundamental mechanisms for controlling log volume in a telemetry pipeline. In this post, I’ll briefly overview log-based metrics, explain why they matter, and provide examples of how to build them using OpenTelemetry and BindPlane OP.