I’ll go out on a limb and venture a guess that everybody reading this blog has had challenges adjusting to life in a post COVID world. I certainly have. My latest challenge? Navigating the simultaneous use of a face mask in public venues while wearing glasses to read stuff that’s more than 20 feet away. Just like ski and water goggles, my prescription specs also fog up when simply taking a leisurely stroll through the grocery store.

This is the story of how I used Honeycomb to troubleshoot redis/redis-rb#924 and discovered a surprising workaround.

This post is the second in our Kubernetes observability tutorial series, where we explore how you can monitor all aspects of your applications running in Kubernetes, including: We’ll cover using Elastic Observability to ingest and analyze container metrics in Kibana using the Metrics app and out-of-the-box dashboards.

A while ago, we covered the invocation (trigger) methods supported by Lambda and the integrations available with the AWS catalog. Now we’re launching a series of articles to correlate these integration possibilities with common serverless architectural patterns (covered by this literature review). In Part I, we will cover the Orchestration & Aggregation category. Subscribe to our newsletter and stay tuned for the next parts of the series.

Cybersecurity has always been an evolving challenge for IT teams—even before the pandemic and subsequent mass exodus to the home office. At the Protect & Streamline Summit for IT Leaders, experts from the IT and cybersecurity space came together to share advice for IT leaders scrambling to contend with a new set of business risks. Read on for key takeaways or access all session recordings for a deeper dive.

Digital Experience firm analyzes employee sentiment on the IT service they’re receiving IT experience management software company Nexthink is revving its efforts to help companies measure and improve how employees feel about their IT environments with a new release of its platform.

Over the past few months, many organizations have transitioned their employees from mostly onsite to fully remote work environments. Now we’re entering into a phase where roughly 30% of the workforce will soon head back to the office, while the rest continue to work from home.

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.