Monitoring began by using software agents to capture data from infrastructure, operating systems, and applications. These agents would collect metrics and events from these systems to understand the health of the underlying system and the applications. This is what infrastructure monitoring is today.
Running software uses computer memory for data structures and executable operations. How this memory is accessed and managed depends on the operating system and the programming language. Many modern programming languages manage memory for you, and Ruby is no different. Ruby manages memory usage using a garbage collector (also called gc). In this post, we’ll examine what you, a Ruby developer, need to know about Ruby’s gc. Use the links below to skip ahead in the tutorial.
In the middle of January 2020, I got a notification about the upcoming Mattermost hackathon that was being hosted on the HackerEarth platform. I checked out the hackathon page but I forgot about it the next day when I went to work. One morning, I was surfing the internet sipping my coffee and landed on a website that discussed why employee churn rate is high in organizations.
ServiceNow® is facing one of the biggest opportunities to date: developing a self-healing IT environment that makes proactive IT support a reality. Although the concept of self-healing has been around for at least a decade, the ability to achieve it has fallen short. A lack of system intelligence stopped us from predicting and preventing many issues without human intervention somewhere in the process. AI technology is changing that paradigm.
If you are using InfluxDB to monitor your data and systems, then alerts may be an essential part of your workflow. We currently have a system for monitoring your data whether it enters a critical or non-critical state. Here I’m going to give a detailed guide on setting up alerts using our InfluxDB Cloud product as well as some best practices for having a good experience using alerts.
In sync with the upcoming release of Splunk’s Machine Learning Toolkit 5.2, we have launched a new release of the Deep Learning Toolkit for Splunk (DLTK) along with a brand new “golden” container image. This includes a few new and exciting algorithm examples which I will cover in part 2 of this blog post series.
In part 1 of this release blog series we introduced the latest version of the Deep Learning Toolkit 3.1 which enables you to connect to Kubernetes and OpenShift. On top of that a brand new “golden image” is available on docker hub to support even more interesting algorithms from the world of machine learning and deep learning! Over the past few months, our customers’ data scientists have asked for various new algorithms and use cases they wanted to tackle with DLTK.
You may already be using Splunk to ingest data and generate alerts and dashboards so you can take quick action on problems, but did you know you can quickly start a VictorOps trial and in three easy steps, have great Splunk alerts escalated to the right teams and people with a mobile app notification, SMS message or a live phone call?
Contact or Call... Center or Centre... Call them what you will, but, wow, we’re calling them and we’re counting on them now more than ever! The dramatic shift to remote everything has flooded Contact Centers with more calls, while also requiring providers to adopt remote, distributed architectures.
For those of you with some gray hair working in the manufacturing business, remember when order intake plunged suddenly by more than 40%? Remember when CFO and Controllers ruled the company, driving painful cost-cutting programs to counter double-digit business losses? It was the time of the Economic and Financial Crisis 2007/08, which forced manufacturing organizations to stare in the abyss.
