To monitor your Elastic Stack with Elastic Cloud on Kubernetes (ECK), you can deploy Metricbeat and Filebeat to collect metrics and logs and send them to the monitoring cluster, as mentioned in this blog. However, this requires understanding and managing the complexity of Beats configuration and Kubernetes role-based access control (RBAC). Now, in ECK 1.7, the Elasticsearch and Kibana resources have been enhanced to let us specify a reference to a monitoring cluster.

Monitoring, troubleshooting, and debugging your code all require logging. It not only makes the underlying execution of your project more visible and understandable, but it also makes the approach more approachable. Intelligent logging procedures can assist everyone in a company or community to stay on the same page about the project's status and progress.

Sysadmins, cartographers, and dashboard designers can now personalize Elastic Maps to create richer geodata stories. The 7.14 release of Elastic Maps has the geo capabilities to highlight points of interest, hide unnecessary details, and help you explore new trends in your data. Elastic Maps is available now on Elastic Cloud — the only hosted Elasticsearch offering to include all of its latest features.

“What happened?” If you’ve never uttered those words, this blog isn’t for you. For those of us in cybersecurity, this pint-sized phrase triggers memories of unforeseen security incidents and long email threads with the CISO. What happened to those security patches? Why didn’t we prevent that intrusion? Organizations tend to lean towards protecting their borders and less towards understanding the importance of overall security hygiene.

Gitlab is the DevOps lifecycle tool of choice for most application developers. It was developed to offer continuous integration and deployment pipeline features on an open-source licensing model. GitLab Runner is an open-source application that is integrated within the GitLab CI/ CD pipeline to automate running jobs in the pipeline. It is written in GoLang, making it platform agnostic. It is installed onto any supported operating system, a locally hosted application environment, or within a container.

In this tutorial, we will go through a working example of a Ruby application auto-instrumented with OpenTelemetry. To keep things simple, we will create a basic “Hello World” application, instrument it with OpenTelemetry’s Ruby client library to generate trace data and send it to an OpenTelemetry Collector. The Collector will then export the trace data to an external distributed tracing analytics tool of our choice.