Operations | Monitoring | ITSM | DevOps | Cloud

Properly testing a service’s APIs to ensure that it can handle production traffic presents many challenges for engineers—SREs need to guarantee the resiliency of their application, while developers must ensure that their features perform well at any given scale. Speedscale is a testing framework built for Kubernetes applications that enables you to load test with real-world production scenarios by replaying actual API traffic that your application has experienced.

The trend of Platform Engineering is now gaining momentum, which analysts and industry experts refer to as one of the most disruptive philosophies of the moment. But regardless of experts’ predictions and assumptions, what matters for organizations today is understanding what adopting an approach such as Platform Engineering actually entails, what a successful solution looks like, and how to adopt best practices for its implementation. That's what this article is about.

There is a common painful workflow with many observability solutions. Each data type is separated into its own user interface, creating a disjointed workflow that increases cognitive load and slows down Mean Time to Diagnose (MTTD). At Coralogix, we aim to give our customers the maximum possible insights for the minimum possible effort. We’ve expanded our APM features (see documentation) to provide deep, contextual insights into applications – but we’ve done something different.

Observability in serverless computing environments, such as AWS Lambda, has always been a challenge.

RAN has incrementally evolved with every generation of mobile telecommunications, thus enabling faster data transfers between user devices and core networks. The amount of data has increased more than ever with an increase in the number of interlinked devices. With existing network architectures, challenges lie in handling increasing workloads with the ability to process, analyse and transfer data faster. The 5G ecosystem requires virtual implementations of RAN.

Many of the benefits of running Kubernetes come from the efficiencies that you get when you share the cluster – and thus the underlying compute and network resources it manages – between multiple services and teams within your organization. Each of these major services or teams that share the cluster are tenants of the cluster – and thus this approach is referred to as multi-tenancy.