At Progress Flowmon, we continue to develop and improve the Flowmon product family. The latest update takes the core Flowmon product to release 12.3 and updates our industry-leading Anomaly Detection System (ADS) to version 12.2. In this blog, we highlight several of the improvements.
No one wants to be on the receiving end of the blame game—especially in the wake of a major incident. Sure, you know you were the one who made the final change that caused the incident. And hopefully, it was a small one that didn’t cause any SEV-1s. Still, the weight of knowing you caused something bad should be enough, right? Unfortunately, sometimes fingers get pointed, your name gets called, and suddenly, everyone knows that you’re the person who created more work for everyone.
Almost every study examining the hourly cost of outages invariably leads to a clear and undeniable conclusion: outages are expensive. According to a 2016 study, the average cost of downtime was estimated at approximately $9,000 per minute. In a more recent study, 61% of respondents stated that outages cost them at least $100,000, with 32% indicating costs of at least $500,000 and 21% reporting expenses of at least $1 million per hour of downtime.
Recently I caught up with Jamie Allen on Episode 67 of the Slight Reliability podcast to discuss the idea of a single pane of glass (SPOG). Jamie had written an article titled The Single Pain of Glass which coincidentally was what I titled Slight Reliability Episode 10. I thought given our shared use of puns and this topic that it was worth a conversation! So, what is a single pane of glass? Is it an idea with practical application? How does it fit into the world of modern observability?
Microsoft Teams has become the go-to platform for seamless collaboration and communication. However, like any technology, performance issues can arise, and these issues affect user experience and productivity. For IT teams tasked with Microsoft Teams troubleshooting, having access to comprehensive data is key. In this blog, we explore the challenges faced by IT teams and how harnessing more data can make the process significantly easier.
A few weeks ago, we published some benchmarking that showed performance gains in InfluxDB 3.0 that are orders of magnitude better than previous versions of InfluxDB – and by extension, other databases as well. There are two key factors that influence these gains: 1. Data ingest, and 2. Data compression. This begs the question, just how did we achieve such drastic improvements in our core database? This post sets out to explain how we accomplished these improvements for anyone interested.
What guarantees the success of a website today isn’t just its content and design; delivering a seamless and efficient user experience (UX) is also extremely critical. This is where Core Web Vitals are important as they provide a collection of performance metrics to evaluate the quality of website user experience. Core Web Vitals are critical to attract visitors and retain them as they directly impact a site’s visibility on Google.
If you work with Kubernetes, you know that any number of issues can pose a serious threat to the stability and security of your deployments. One that's subtly damaging is configuration drift, which occurs when the actual state of how your system is set up — its configuration — strays from the way you defined. Configuration drift in Kubernetes can happen when people make changes manually, systems aren't synchronized properly or monitoring falls short.
