In the world of datacenter management, it’s not uncommon to encounter a complex maze of networks and servers. Over time, these environments can become fragmented and difficult to navigate, making it challenging to maintain a comprehensive inventory of your IT assets.

I recently had the privilege of attending KubeCon + CloudNativeCon Europe 2024 in Paris. The conference, hosted by the Linux Foundation, marked the 10th anniversary of Kubernetes. Here are the key takeaways and highlights from the conference.

Digital media compression serves as the cornerstone of data reduction and efficiency enhancement in the digital realm. Its significance lies in its ability to minimize file sizes, thereby saving storage space and reducing bandwidth consumption during data transmission. This foundational step is crucial for managing the ever-expanding volumes of digital content, from high-resolution images and videos to extensive databases.

During the early periods of cloud computing, most organizations used single-location data centers. These single-location data centers often faced higher risks of downtime and service disruption due to localized disasters or hardware failures. As a solution to these problems, cloud services like AWS introduced the concept of availability zones. This introduction was an important milestone in the evolution of cloud computing, as it facilitated high availability through geographic distribution.

If you're working in IT monitoring and observability, you simply cannot ignore the power of Datadog and New Relic. These two tools have plenty of features that can revolutionize your entire observability strategy and give you complete control over your infrastructure. These tools are built so as to capture the tiniest of details, be it on applications, infrastructure, databases, servers, or something completely on the cloud.

Ever wanted to make sure a promise only runs if some other promise has succeeded? Consider this contrived example with two reports type promises, It's Friday! and I love CFEngine Feature Friday. Per normal ordering1, these two promises will be emitted in the written order. /tmp/feature-friday-7.cf command output If we want them in the opposite order, we could either change the order or define classes based on the results of the promises.

Regardless of how much effort teams put into developing, training, and evaluating ML models before they deploy, their functionality inevitably degrades over time due to several factors. Unlike with conventional applications, even subtle trends in the production environment a model operates in can radically alter its behavior. This is especially true of more advanced models that use deep learning and other non-deterministic techniques.

Kubernetes annotations are key-value pairs attached to Kubernetes objects, providing a flexible way to extend the functionality of your Kubernetes resources without altering their internal specifications. These annotations serve as a tool to store additional metadata to tailor behavior, orchestrate tools, and interact seamlessly with third-party utilities that complement your Kubernetes environment.

In our webinar, Mastering Telemetry Pipelines: A DevOps Lifecycle Approach to Data Management, hosted by Mezmo’s Bill Balnave, VP of Technical Services, and Bill Meyer, Principal Solutions Engineer, we showcased a unique data-engineering approach to telemetry data management that comprises three phases: Understand, Optimize, and Respond.

After decades as a software engineer, I’ve seen firsthand how complexity can grow exponentially with scale. Thankfully, over that time, quite a few tools have come along to help manage some of that complexity. One great example is the Internal Developer Portals (IDPs), which were built to connect an engineering organization’s architecture, processes, documentation, and definitions and alignment to standards of health.