Operations | Monitoring | ITSM | DevOps | Cloud

When I first had the idea for this post, I wanted to provide a collection of actionable ways to handle errors caused by API rate limits in your applications. But as it turns out, it’s not that straightforward (is it ever?). API rate limiting is a minefield, and at the time of writing, there are no published standards in terms of how to build and consume APIs that implement rate limiting.

If you’re using Grafana and Kubernetes, we’ve got exciting news — Grafana Labs will be maintaining and managing the Grafana Operator, the open source Kubernetes operator that helps you manage your Grafana instances within and outside of Kubernetes. This significant move not only elevates the Grafana Operator to an officially supported tool but also cements its place as a staple for managing Grafana as code, especially for users keen on adopting GitOps principles.

When monitoring applications with streaming data pipelines, there are additional complexities to consider that are not present in traditional batch-processing systems. Whether you’re using streaming data pipelines to power a digital trading platform, capture sensor data from an IoT device, or recommend news articles to users, it can be challenging to identify the root cause of delays when you’re dealing with distributed systems, real-time data, and the dynamic nature of events.

Today, any revenue-generating website is like a house of cards, poised to collapse with multiple points of failure. The modern service delivery chain relies on intricate multi-step transactions and third-party API integrations, making the system more complex and interconnected. A single point of failure in the architectural diagram above can lead to slowdowns and outages with tangible consequences on your bottom line.

If your company’s IT infrastructure includes Dell devices, then you need to find a way to do Dell warranty checks efficiently. Sure, you can do it manually, computer by computer. But if you have just received a large batch of them, you can be looking at various days of work ahead.

Linux is a powerful and versatile operating system that’s widely recognized for its robustness, security, and open-source nature. It is a Unix-like OS, which means it shares many characteristics with the original Unix system developed in the 1970s. Linux has become a preferred choice in various computing environments, from personal computers and servers to embedded systems and supercomputers.

Organizations today have seen a significant increase in endpoints due to the rapid growth of remote and hybrid work, along with the emerging trend for employees to bring their own devices to work. With all these additional endpoints to keep track of and manage, it has become all the more necessary for IT managers and cybersecurity professionals to implement or upgrade their endpoint management software to ensure that all devices are secure and up to date.

Splunk’s recent update to its Machine Learning Toolkit (MLTK) is a good reason to spend a few paragraphs thinking through the links between Observability and machine learning. First, let us quickly review what Observability and machine learning are.

If you’ve been building web apps long enough, you’ve certainly read through your fair share of logs. One of the more painful parts of going through web app logs is getting them all to the same place. It’s only then you can review your logs and troubleshoot your applications. In this article, we’ll build a simple Go web app to send logs to SolarWinds® Loggly®.

DevOps Insights with Dennis: Efficient DevOps - GitLab CI/CD for Docker Builds and ArgoCD Deployments on Kubernetes.