Brief introduction to understanding Kubernetes basics Kubernetes is a broad platform that consists of more than a dozen different tools and components. Among the most important are: If you use Kubernetes to manage containers, this will require a container runtime, which is the software that runs individual containers. Kubernetes supports a number of container runtimes; the most popular are Docker, containerd, and cri-o.

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The OpenTelemetry Collector is a new, vendor-agnostic agent that can receive and send metrics and traces of many formats. It is a powerful tool in a cloud-native observability stack, especially when you have apps using multiple distributed tracing formats, like Zipkin and Jaeger; or, you want to send data to multiple backends like an in-house solution and a vendor. This article will walk you through configuring and deploying the OpenTelemetry Collector for such scenarios.

“Kubernetes vs. Docker” is a phrase that you hear more and more these days as Kubernetes becomes ever more popular as a container orchestration solution. However, “Kubernetes vs. Docker” is also a somewhat misleading phrase. When you break it down, these words don’t mean what many people intend them to mean, because Docker and Kubernetes aren’t direct competitors.

Persistence is effectively the ability of the attacker to maintain access to a compromised host through intermittent network access, system reboots, and (to a certain degree) remediation activities. The ability of an attacker to compromise a system or network and successfully carry out their objectives typically relies on their ability to maintain some sort of persistence on the target system/network.

Last week Sumo Logic announced our new Observability Suite, which included the public introduction of the closed beta for our distributed tracing capabilities as part of our Microservices Observability solution. This new solution will provide end-to-end visibility into user transactions across services, as well as seamless integration into performance metrics and logs to accelerate issue resolution and root-cause analysis. In this blog, we’ll explore the new solution in detail.

Today, we made an important announcement that builds on and extends our category leadership in Continuous Intelligence. This blog is to put that announcement into context for our customers, partners and the broader industry. In 2017, we shared our strategy to democratize machine data by building a cloud-native, Continuous Intelligence Platform™ to 1) unify modern application and machine data; 2) support the ongoing proliferation of users and access; and 3) leverage cloud-scale economics.

As more and more applications move to the cloud, the complexity of application architectures inevitably increases. It is a burden we willingly take on because the benefits—flexible deployment, technology diversity, independent scaling, and much more— tend to far outweigh the costs. But along this transition, most organizations face a dilemma, to divert resources to the necessary tooling for effective monitoring and troubleshooting of these systems – i.e.