We often hear the term "observability" in the context of DevOps and how SREs use telemetry data. Collecting and analyzing this telemetry data is a vital first step to a successful autonomous IT operations strategy. Observability can help you find out about problems in your system you didn’t know you had—and before your users are impacted—by giving you new visibility that your monitoring systems don’t provide. But any observability initiative must also include network observability.

Kubernetes 1.31 brings a plethora of enhancements, including 37 line items tracked as ‘Graduating’ in this release. From these, 11 enhancements are graduating to stable, including the highly anticipated AppArmor support for Kubernetes, which includes the ability to specify an AppArmor profile for a container or pod in the API, and have that profile applied by the container runtime.

At Mezmo, we consider Understand, Optimize, and Respond, the three tenets that help control telemetry data and maximize the value derived from it. We have previously discussed data Understanding and Optimization in depth. This blog discusses the need for responsive pipelines and what it takes to design them.

Let’s dive into the world of Objective Monitors in the context of Active Directory (AD) servers and explore why monitoring them using DX Unified Infrastructure Management (UIM) is crucial.

Your database is more powerful than you think. Learn how built-in vector capabilities can power your GenAI applications and save you from the hassle of adopting a new database. The heart of Generative AI (GenAI) workloads rely on the ability of computers to categorize and understand the world's data (images, sounds, text) as numerical representations called vectors. This is achieved through a process called "embedding," where a model translates the data into vectors.