Operations | Monitoring | ITSM | DevOps | Cloud

Kubernetes can be tough to troubleshoot and remediate fast, especially when you have many interdependent services. This blog, part 3 of 3 in the “8 SRE Best Practices to Help Developers Troubleshoot Kubernetes” series, describes the Kubernetes observability foundation StackState has built to support SRE best practices and enable rapid remediation of issues.

Telemetry pipelines are a modern approach to monitoring and analyzing systems that collect, process, and analyze data from different sources (like metrics, traces, and logs). They are designed to provide a comprehensive view of the system’s behavior and identify issues quickly. Data transformation is a key aspect of telemetry pipelines, as it allows for the modification and shaping of data in order to make it more useful for monitoring and analysis.

LM Logs offer intelligent log analysis with querying capabilities for all experience levels to analyze log data. But it’s most effective to know when to investigate deeper and conduct further analysis instead of trying to identify hidden trends in log data manually. The best way to determine what’s relevant now is to see if the amount of log data and message types produced in a device or service have drastically changed.

Kubernetes has emerged as the de facto standard for container orchestration in modern software development, allowing organizations to manage and scale containerized applications easily. As a highly dynamic and distributed system, however, Kubernetes can be challenging to manage and maintain at scale. One of the most critical aspects of maintaining a stable and secure Kubernetes cluster is monitoring the object configurations and tracking the changes over a period of time.

In industrial operations, time is money. The more efficient processes and machinery are, the better it is for business. Providing proactive monitoring and maintenance of industrial machines, however, is not an easy task. This is especially true as these machines become increasingly complex and distributed. It’s not possible to have maintenance crews on site for every asset in a distributed system. The edge is where the physical world meets the digital world.

Prometheus is the de facto open-source solution for collecting and monitoring metrics data. Its straightforward architecture, operational reliability, minimal upfront cost, and versatility in integrating with cloud-native systems make it the preferred choice for many. Getting started is as simple as configuring the Prometheus server and setting simple parameters such as the scrape intervals and targets, cadence, and setting the job name based on the function of the server.

Google Cloud Operations is a powerful integrated monitoring, logging, and trace managed service for applications and systems running on Google Cloud and beyond. As part of our partnership with Google, we help extend Cloud Operations with BindPlane OP and OpenTelemetry monitoring for a complete monitoring solution. With BindPlane OP, Google Cloud Operations becomes a single pane of glass for monitoring all aspects of your data center, no matter if it’s on prem or running in the cloud.

Whether you run a website of your own, or rely on a specific website for your profession, finding out a URL has gone down can cause considerable losses in revenue and accessibility, or deny access to the critical information you or your users rely on. Uptime monitors let you constantly check your site(s) to see if they are up and running. There are a few common use cases for uptime monitoring.

I want to make my microservices more observable. Currently, I only have logs. I’ll add metrics soon, but I’m not really sure if there is a set path you follow. Is there a beginner's guide to observability of some sort, or best practice, like you have to have x kinds of metrics? I just want to know what all possibilities are out there. I am very new to this space.