Operations | Monitoring | ITSM | DevOps | Cloud

Observability is a mindset that lets you use data to answer questions about business processes. In short, collecting as much data as possible from the components of your business — including applications and key business metrics — then using an AI-powered tool to help consolidate and make sense of this huge volume of data gives you observability into your business. Having observability for your business and applications lets you make smarter decisions, faster.

Recently, we hosted a great joint webinar with the team from AVD TechFest to present the results of a survey we conducted jointly to assess real-world Microsoft Azure Virtual Desktop (AVD) usage and industry and customer sentiments towards the AVD technologies. Alongside myself, Peter Claridge from eG Innovations and Simon Binder, digital workplace architect at Cygate and co-founder of the community-oriented AVD TechFest, were answering Azure Virtual Desktop questions.

Agent-based and agentless monitoring are the two main approaches network monitoring tools use to capture and report data from network devices. As the names suggest, the difference between the two is pretty simple: someone has to install extra software(the agent) for agent-based monitoring to work. But, that doesn’t explain why an IT team or an MSP would choose agent-based or agentless monitoring.

ITOps and DevOps are technology management practices that have been around long enough that anyone in IT should have a good grasp of what they mean. Here’s our experts’ take on ITOps vs. DevOps.

Log tracking, trace log, or logging traces… Although these three terms are easy to interchange (the wordplay certainly doesn’t help!), compare tracing vs. logging, and you’ll find they are quite distinct. Logs, traces, and metrics are the three pillars of observability, and they all work together to measure application performance effectively. Let’s first understand what logging is.

In a DevOps environment, continuous testing is essential to success. By automating the testing process, you can release new, bug-free code faster, and more efficiently. In this software development tutorial, we will examine continuous testing, its benefits, and best practices.

Dashboards allow you to visualize and correlate monitoring data from across disparate data sources, technologies, and infrastructure components to understand what’s going on in your environment. In a growing organization, it’s paramount to standardize how teams build their dashboards to ensure their consistency and legibility.

Earlier this year we introduced the world to Grafana Mimir, a highly scalable open source time series database for Prometheus. One of Mimir’s guarantees is 100% compatibility with PromQL, which it achieves by reusing the Prometheus PromQL engine. However, the execution of a query in the Prometheus PromQL engine is only performed in a single thread, so no matter how many CPU cores you throw at it, it will only ever use one core to run a single query.

We are introducing a change to the pull request merge checks that will make them more reliable. Specifically, we will no longer allow pull requests to be merged while a build is in progress. It was possible for a pull request to be merged while some of its builds were still in progress and for those builds to fail after the merge has completed. This created an undesirable situation if build merge checks were enabled.