Operations | Monitoring | ITSM | DevOps | Cloud

Enterprise use of Microsoft’s Azure Cloud Services is expanding at an unprecedented rate as cloud computing usage expands. However, enterprises that have recently switched to Azure continue to have serious concerns about monitoring their applications efficiently. As the business overgrows, it is essential to understand its functioning and health simultaneously.

Over the last 18 months at incident.io, we’ve done a lot of migrations. Often, a new feature requires a change to our existing data model. For us to be successful, it’s important that we can seamlessly transition from the old world to the new as quickly as we can. There are few things in software where I’d advocate a ‘one true way,’ but the closest I come is probably migrations. There’s a playbook that we follow to give us the best odds of a smooth switchover.

A cornerstone of network observability is the ability to ask any question of your network. That means having an unbound capacity to explore the tremendous amount and variety of network telemetry you collect. It means seeing trends and patterns from a macro level, but it also means getting very granular to pursue any line of analysis of your data. Collecting information from flow records, SNMP, streaming telemetry, BGP, eBPF, and so on is indeed very important.

Using the cloud can be challenging. Everyone likes to tell stories about a surprise cloud bill they received and laugh, but let’s be honest: it’s happened to all of us. When dealing with complex cloud environments and cloud-native architectures, gaining visibility into the state of the environment is critical. Today, developers are looking to monitor how things are working and performing as part of every step in the software development life cycle.

We are excited to announce the early preview of Calico Enterprise 3.16. This latest release extends the active security platform’s support for egress access controls, improves the usability of network-based threat defense features, and scales visualization of Kubernetes workloads to 100s of namespaces. Let’s go through some of the highlights of this release.

Imagine being able to mimic a celebrity’s look with a screenshot. Users could use the image to quickly find clothing sold online that matches the style. But, this is not the search experience of today. Customers struggle to find what they need, and if they can’t, they'll leave. Some of them don't remember the name (keyword) of what they are looking for, but have an idea of what it looks like or the actual image.

For businesses and organizations that need reliable, secure servers to host data, applications, or services around the world, Linux is often the platform of choice. From powering websites to managing network infrastructure to running enterprise databases, there are countless reasons people have turned to Linux as their go-to server solution over the past few decades.

Grafana 9.4 is here! Get Grafana 9.4 With the latest Grafana release, we’re introducing a wealth of new features and improvements that makes getting started with Grafana even easier and that take your visualizations and observability best practices to the next level. In addition to enabling TraceQL, the new query language for distributed tracing in Grafana Tempo 2.0, for all Grafana Cloud users, the Grafana 9.4 release comes with a fresh round of features.

Meeting bandwidth capacity needs of customers is a crucial business objective for today’s providers. While both coarse wavelength-division multiplexing (CWDM) and dense wavelength-division multiplexing (DWDM) are modern forms of wavelength-division multiplexing (WDM) that effectively solve those increasing bandwidth needs by upgrading the utilization of new and existing fiber, they are each designed to tackle different network challenges.

On their own, enterprise applications and systems are not always straightforward. Writ large, they are complex, integrated environments, full of multiple data formats and structures. You spend a great deal of effort and time to define and maintain diverse data models among these integrated components. A Canonical Data Model helps reduce that burden significantly — by promoting a standard and consistent data model between connecting components. This article describes a few things to get you started.