Operations | Monitoring | ITSM | DevOps | Cloud

Organizations all over the world have been moving workloads to the cloud. Microsoft Azure provides a host of benefits and services which make it a top choice for on-premises to cloud migrations. When it comes to migrating workloads, Azure virtual machines are a great choice with VM options up to 416 vCPUs and 12 TB of memory and storage IOPS up to 3.7 million.

Too often, when organizations migrate workloads to the cloud or build new cloud-native applications, they don’t really think about storage. The cloud provider takes care of all that, right? Well, yes and no. There are cost implications to cloud storage that many don’t adequately anticipate—until they get the bill, that is.

We often speak to organizations about Azure and one of the common questions is what is the difference between App Insights and Log Analytics. In this article we will aim to discuss those differences and overlaps. If we think about the Azure Monitoring Platform / ecosystem, then Log Analytics and App Insights both play a role within that platform as shown below. Log Analytics plays a role in the storage of Log Data and the analysis of Log Data.

Before Megaport, MetTel’s customers battled impaired application performance and deployment delays when moving to the cloud. Now, their connectivity enables high-performance applications in the most distributed cloud environments. Here’s how.

Yesterday, GitHub changed how the archives they provided are made. The result of this change surprised developers, triggering pipeline failures all over the world in most ecosystems. According to this GitHub post, this is a consequence of recent changes to Git itself, released almost six months ago and just deployed within GitHub now with unforeseen impact. This change has thankfully been retracted.

When it comes to cloud costs, engineers have a particular reputation. The executive suite and finance department often share perplexing frustration about why engineers appear not to care about budgets and costs at all. They provide hard budget limits, yet engineers frequently surpass them, citing performance and user experience as reasons for the excess spending.

Building digital platforms, and platform engineering teams to support them, isn’t new. The platform approach first emerged more than 20 years ago and predates the DevOps movement that began in the mid-2000s. But the recent surge in popularity of platform engineering has caused some to argue that “DevOps is dead” and modern platform engineering has replaced it. Here’s what we found in the 2023 State of DevOps Report to counter that argument.

Kubernetes turns 9 this year and with its maturity each year, it brings new challenges that drive seismic influence across the rapidly changing cloud native ecosystem. Each year we see new tools created and existing solutions optimized from new lightweight distributions, new features across Kubernetes management platforms, and container security solutions, all adding value to users but simultaneously contributing to the complexity are facing to run Kubernetes successfully.