Opensource

Building AI for Observability with AWS Bedrock In my previous post, I wrote about closing the last mile of observability with AI . The core idea was simple: we already have plenty of metrics, logs, and traces, but the real challenge is turning them into insights and answers that engineers can act on. In that post, I highlighted two main gaps: Connector layer – bridging multiple observability tools like Prometheus, Thanos, Elastic, etc. Insight layer – going beyond raw queries to provide real context and recommendations. Now, I’ve been experimenting with AWS Bedrock , and it feels like a natural way to solve both layers. ...

Over the years, observability has grown in ways I couldn’t have imagined when I first started working in this space. Thanks to OpenTelemetry, we now have a standard way to collect traces, metrics, and logs. Tools like Grafana, Prometheus, Jaeger and Elasticsearch make it easy to store and visualize that data. But here’s the truth I keep coming back to: Even with all the dashboards and alerts, something is still missing. ...

Summary Monitoring in Kubernetes is a complex task. The traditional monitoring framework is not sufficient to handle such a massive workload. Zabbix since version 6.0 provides a native way of integration for monitoring Kubernetes cluster. Zabbix-Kubernetes integration provides various templates to monitor kubernetes components like kube-controller-manager, kube-apiserver, kube-scheduler, kubelet, etc. It also supports automatic discovery of kubernetes nodes, pods and also collects metrics agentlessly. Why I don’t like the Zabbix’s direct way of monitoring Kubernetes cluster? Although Zabbix-Kubernetes integration looks promising in the beginning , it is not easy to use. ...

Summary I joined State Bank of India on July 2020 and worked for 1 year and 2 months as Manager - IT Infrastructure Architect in Enterprise and Technology Architecture department in GITC, Navi Mumbai. My role in SBI is technical, and it involves consulting, design, Architecture and reviewing of application and infrastructure. Through out my tenure in SBI i worked on various short term projects. Among them setting up DevOps infrastructure for SBI is one. ...

Plotly4Nagios is a nagios plugin to display the performance data in Graph. It uses the RRD database provided by pnp4nagios and visualize it in interactive graph format using plotly javascript. The first pre-release is published today in github and here is the installation document. You can experiment it and report the issue/feedback for further enhancement. Plotly4Nagios is accepted and listed under official nagios addons GIT badges Features Easy integration with nagios notes_url. Single page view for all performance metrics. Easy template change using configuration variable. Docker container based deploy and run. Prerequisite pnp4nagios Installation Download plotly4nagios.tar.gz and extract it under /usr/local/plotly4nagios Modify the config.json variables according to the environment Copy the plotly4nagios/plotly4nagios.conf to /etc/http/conf.d/ folder and restart httpd Add the follwing with notes_url to templates.cfg. notes_url /plotly4nagios/plotly4nagios.html?host=\$HOSTNAME\$&srv=_HOST_ notes_url /plotly4nagios/plotly4nagios.html?host=\$HOSTNAME$&srv=\$SERVICEDESC$ Restart httpd and nagios. Installation with docker(Ubuntu image) Build the docker image using the below command git clone https://github.com/vigneshragupathy/plotly4nagios.git cd plotly4nagios docker build -t plotly4nagios . Run the docker container using the below command docker run -it --name plotly4nagios -p 80:80 plotly4nagios Alternatively direct pull and run from docker hub. ...