Operations | Monitoring | ITSM | DevOps | Cloud

In 2009 Node.js opened up a door for front-end developers to dip their toes into the world of servers without having to leave the comfort of their language. It’s almost effortless to get started with Node. You can basically copy-paste an entire HTTP server into existence and then install an ODM and you’ve got your CRUD app ready to roll! However, if we’ve learned anything from the amazing Spider-Man, it’s that with great power, comes great responsibility.

As the adoption of GraphQL is rising steadily, teams often come across the natural question of how to deal with errors that pass service boundaries and propagate from API endpoints to clients and consumers.

If your Helm charts could talk, what would they say to potential users? Would they boast of the power in the Kubernetes apps they deploy? Would they warn of their dangers? Would they offer advice? In JFrog’s new ChartCenter, a community repository of publicly available Helm charts, that’s exactly what they’ll do. ChartCenter reveals to users what known risks lie within the container images deployed by every Helm chart.

In late 2019 we were running our first ever user survey on Icinga. We always strive to get to know our users and their requirements better. Our goal for the survey was to get a general understanding about how Icinga is being used. The results are very interesting for us and will help us to make decisions. Today we want to share the results of the survey.

Examine. Search. Browse. Icinga, in Zulu language. The origin of the names of many software are somewhat strange (or unknown). In fact, that would be enough material for a whole different article. This time here I come to propose Pandora FMS as an alternative to Icinga and, if you are a frequent reader of ours, it may ring a bell.

Modern cloud storage tools such as Dropbox give teams the ability to easily share and centralize content, conveniently collaborate on projects, and sync data across devices. They’ve proven to be real productivity enhancers, especially with the expansion of work-from-home workforces. But cloud storage tools often end up being a dumping ground for lots of content and various clutter, making it clumsy at best (and next to impossible at worst) to find anything.

The 7.6 release of the Elastic Stack delivered the last piece required for an end-to-end machine learning pipeline. Previously, machine learning focused on unsupervised techniques with anomaly detection. However, several features have been released over the 7.x releases. In 7.2 Elasticsearch released transforms for turning raw indices into a feature index. Then 7.3, 7.4, and 7.5 released outlier detection, regression, and classification, respectively.

Clinical communication and collaboration (CC&C) systems enhance care coordination to improve the patient experience. The systems are equipped with secure mobile messaging, allowing care teams to ditch their insecure pagers for HIPAA-compliant smartphone applications. Gartner, the global leader in tech research, has released its Hype Cycle for Real-Time Health System (RTHS) Technologies, 2020.

Discover how DevOps and SREs can develop more and operate less by applying AI to events, metrics, traces and logs to keep CI/CD agile and your business growing.

There are two ways you can view the current mappings on your Logit ELK Stacks. One way is to use dev tools in Kibana. You can access Kibana from any of your dashboards by choosing from your dashboard Stack settings > Access Kibana. You can also search for a specific mapping of an Index name. For example if we wanted to see the mappings for the a Filebeat index name we can run the following to return only the desired mappings.