As odd as it might sound, I think these past few months have done a lot of good for IT, and following the recent news from Nexthink last week, I actually feel optimistic for many enterprises out there that might be struggling. Hear me out. Right now, there are millions of people working in new, flexible work environments that didn’t even exist six months ago.
This was originally posted on The New Stack. Once upon a time, log management was relatively straightforward. The volume, types, and structures of logs were simple and manageable. However, over the past few years, all of this simplicity has gone out the window. Thanks to the shift toward cloud native technologies—such as loosely coupled services, microservices architectures, and technologies like containers and Kubernetes—the log management strategies of the past no longer suffice.
When you want to spot hosts, applications, containers, plant equipment, or sensors that are behaving differently from others, you can use the Median Absolute Deviation (MAD) algorithm to identify when a time series is “deviating from the pack”. In this tutorial, we’ll identify anomalous hosts using mad() — the Flux implementation of MAD — from a Third Party Flux Package called anaisdg/anomalydetection.
In a post-pandemic world, we must use data in new ways. This in turn will require new discussions about, and practices creating, trust and transparency. The necessity of data and its benefits will be weighed against legitimate concerns of misuse of data.
With the new normal adding several more challenges and variables to the security layer, how do you ensure your data is safeguarded without increasing the workload or the headcount of your security team? Using advanced analytics, in tandem with endpoint monitoring applications such as ManageEngine’s Mobile Device Manager Plus and Desktop Central, will help you better visualize and analyze your endpoint data, identify patterns, and establish correlations.
Apache Ignite is a computing platform for storing and processing large datasets in memory. Ignite can leverage hardware RAM as both a caching and storage layer to serve as a distributed, in-memory database or data grid. This allows Ignite to ingest and process complex datasets—such as those from real-time machine learning and analytics systems—in parallel and at faster speeds than traditional databases supported by only disk storage.
As healthcare and life sciences organizations move to digitize their operations and automate their workflows, they are encountering new challenges related to HIPAA regulations, which dictate how patient information should be collected and stored.
Metrics and logs have been around for a long time, yet we haven’t adopted common standards for them. Sure, there have been attempts on the metric side with OpenMetrics. Similarly, tracing only got a standardization effort with OpenTracing just a few years ago. There was no effort in a unified approach to standardize all observability signals until OpenTelemetry began a little less than two years ago. And there has been a need.
As part of our SSL certificate monitoring, we check a lot of things. The usuals, like if it covers the right domain name or if it hasn't expired are, of course, already included. But SSL certificates can get quite complex. Sometimes, SSL certificates get revoked by the issuer. When that happens, browsers worldwide stop trusting them and will throw an invalid certificate warning.
