Zabbix has been part of my toolbox for quite some time. I can easily say it's an indispensable tool for me now.
Managing a dozen servers without Zabbix would be unimaginable. I'm monitoring all of this: CPU, Memory, Hard-drives, website response times, downtime. The UI might be a bit "old school", but everything works flawlessly.
With regards to hard-drive monitoring, I love the machine learning option that allows you to "predict" the number of days before running out of space. That's quite helpful, as I've got some of my servers down due to running out of space multiple times in the past (before I was using Zabbix).
Based on our record, Google Kubernetes Engine should be more popular than Zabbix. It has been mentiond 49 times since March 2021. We are tracking product recommendations and mentions on various public social media platforms and blogs. They can help you identify which product is more popular and what people think of it.
Official Zabbix trainings, documentation on zabbix.com ? Source: over 2 years ago
Hallo, do you know a howto to install zabbix on an ubuntu 20.04 ? I tried the manuals from zabbix.com for MySQL Apache but it didn't work. Source: almost 3 years ago
He suggested that I indeed should set up a home-lab. To be specific he said that I should create a minimal install of Centos 8 and install zabbix server on it (https://zabbix.com) and monitor a whole bunch of other VMs, services and stuff.. He said that I should set up a variety of VMs and also maybe host a website on one of them. And then if I was able to do that, I could help to share a load of zabbix related... Source: about 3 years ago
This is a fresh 21.10 install, using the install repo as detailed on the zabbix.com download page. Source: about 3 years ago
Well, if you can't find anyone, I am more than happy to fill the slot with something regarding Zabbix - just let me know ;). Source: over 3 years ago
Integration with Google Kubernetes Engine (GKE), which supports up to 65,000 nodes per cluster, facilitating robust AI infrastructure. - Source: dev.to / about 2 months ago
In my previous post, we explored how LangChain simplifies the development of AI-powered applications. We saw how its modularity, flexibility, and extensibility make it a powerful tool for working with large language models (LLMs) like Gemini. Now, let's take it a step further and see how we can deploy and scale our LangChain applications using the robust infrastructure of Google Kubernetes Engine (GKE) and the... - Source: dev.to / 3 months ago
Kubernetes cluster: You need a running Kubernetes cluster that supports persistent volumes. You can use a local cluster, like kind or Minikube, or a cloud-based solution, like GKE%20orEKS or EKS. The cluster should expose ports 80 (HTTP) and 443 (HTTPS) for external access. Persistent storage should be configured to retain Keycloak data (e.g., user credentials, sessions) across restarts. - Source: dev.to / 5 months ago
In a later post, I will take a look at how you can use LangChain to connect to a local Gemma instance, all running in a Google Kubernetes Engine (GKE) cluster. - Source: dev.to / 7 months ago
Google Kubernetes Engine (GKE) is another managed Kubernetes service that lets you spin up new cloud clusters on demand. It's specifically designed to help you run Kubernetes workloads without specialist Kubernetes expertise, and it includes a range of optional features that provide more automation for admin tasks. These include powerful capabilities around governance, compliance, security, and configuration... - Source: dev.to / 11 months ago
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