Operations | Monitoring | ITSM | DevOps | Cloud

Building full-stack applications can be challenging, especially when developing the backend and frontend at the same time. In this scenario, frontend teams may have to wait for the backend team to finish building an API before they implement. This is where Mirage.js comes in. In this tutorial, you will explore how to use Mirage.js in frontend applications and mock backend requests for services that have not yet been developed.

Over the past five years, enterprise networking has undergone a significant transformation driven by advancements in technology, the rise of cloud and SaaS applications, the decentralization of the workforce, and the need for agility, scalability, and cost mitigation. These factors have led organizations to shift from on-premise network management systems (NMS) to cloud-managed networking platforms and to adopt technologies like Software-Defined Wide Area Networking (SD-WAN).

Azure Event Grid domain is a fully managed event routing service that simplifies the development of event-based applications by providing a centralized event routing service for cloud-based applications and services.

Graphite is used by many organizations to track and visualize various metrics that their applications or servers send out. But what happens if there are too many of these metrics or the company doesn't want to use its human resources to monitor the behaviour of metrics constantly? In this article, we will use Hosted Graphite by MetricFire to learn about Graphite's ability to notify users about the abnormal behaviour of services or infrastructure in a timely manner.

As we are now 6 months into the year, I thought this would be a great time to sit and reflect on everything that has happened at Civo so far (and some of the plans we have in the pipeline). If you’re interested in taking a look at our previous roadmaps.

CloudZero’s research has determined that as many as 60% of SaaS business leaders feel their cloud spend is higher than it should be. Considering cloud spend is a dominant portion of a SaaS company’s operating costs, unexpectedly high cloud bills can be a significant problem. While many SaaS leaders use discount plans to stem the bleeding, we believe the better solution lies with giving your engineers the tools to track and optimize costs as they build.

Every team has guardrails, whether you recognize them or not. They’re a form of automation that can have significant impact on your software development process and the people doing the work. They’re another way to give toil the boot and keep developers in the flow. We’ve made the case for engineering automation in a previous article; here’s how guardrails as automations ensure that agreed upon boundaries and ways of working are codified into team processes.

Data scientists and machine learning engineers are often looking for tools that could ease their work. Kubeflow and MLFlow are two of the most popular open-source tools in the machine learning operations (MLOps) space. They are often considered when kickstarting a new AI/ML initiative, so comparisons between them are not surprising. This blog covers a very controversial topic, answering a question that many people from the industry have: Kubeflow vs MLFlow: Which one is better?