Operations | Monitoring | ITSM | DevOps | Cloud

Status pages have evolved quite a bit over the last few years. Not that long ago you would have been lucky to find a simple up/down indicator, but today organizations are truly embracing transparency. They are providing their end users with critical metrics, incident data, maintenance calendars and even the status of their own service provider technologies.

Jupyter Notebooks are wonderful because they provide a way to share code, explanations, and visualizations in the same place. Notebooks add narrative to computation. The cells compartmentalize steps and reduce the fear or hesitation associated with editing code. In this way, notebooks act as an invitation for experimentation. Today, I want to extend that invitation and apply it to InfluxDB. In this post, we’ll learn how to query our system stats data from InfluxDB v2.0 using Flux.

Remember when you wanted great alerts, so you read our past two blogs about cyclical statistical forecasts and anomalies? Hopefully, the techniques in those blogs gave you some great results. Here we’re going to show you another way of finding anomalies in your data using a slightly different technique.

Over the past 24 months or so, I have been studying investing/trading while also working to become more proficient with Splunk. I like to combine activities and gain momentum, so I decided stock market and economic data would be the perfect way to dig deeper into Splunk and hopefully improve my investing/trading. In the beginning, I only looked at it as a way to learn more about Splunk while using data that was interesting to me.

In so many ways, speed is a security analyst’s best friend. From threat detection to containment to response – the faster you are, the more secure your business will be. It’s exactly why metrics like dwell time, MTTD (mean time to detect) and MTTR (mean time to respond) exist. It’s a barometer for the strength of your organization’s security, and a gauge of success for any good security team.