This year’s Experience Everywhere is in the books – and just as we expected, the New York crowd helped us end this unforgettable four-city tour on a high note. We knew we had to bring something special for our big return to a live conference, and our incredible attendees and a star-studded lineup of speakers helped us do just that.
At ObservabilityCON, we announced our first step towards launching a native integration between Grafana k6 load testing and Grafana Tempo tracing (k6 x Tempo) in Grafana Cloud. We created k6 x Tempo to help dev, testing, and operation teams analyze their performance test results more effectively and proactively improve the reliability of their business-critical applications.
Google Kubernetes Engine (GKE) is a managed Kubernetes service that enables users to deploy and orchestrate containerized applications on Google’s infrastructure. Datadog’s GKE integration, when paired with our Kubernetes integration, has always provided deep visibility into the health and performance of your clusters at the node, pod, container, and application levels.
Have you been meaning to learn about OpenTelemetry and the integration of all available application and service telemetry? If you like to learn things by doing; get ready to dive in and have some fun with OpenTelemetry and Splunk Observability Cloud. Quickly learn more about OpenTelemetry auto-instrumentation and collectors at your own pace with these walkthroughs and guides.
Artificial intelligence for IT operations (AIOps) is an umbrella term for the use of big data analytics, machine learning (ML) and other artificial intelligence (AI) technologies to automate the identification and resolution of common IT issues. The systems, services and applications in a large enterprise produce immense volumes of log and performance data. AIOps uses this data to monitor assets and gain visibility into dependencies within and outside of IT systems.
Handling today’s network performance challenges is imperative. Especially when there’s no specific tool for proactive monitoring, results from a root cause analysis are often incomplete. Organizations that don’t review metrics related to performance and availability risk compromising their network.
Everyone’s talking about Platform Engineering these days. Even Gartner recently featured it in its Hype Cycle for Software Engineering 2022. But what is Platform Engineering really about? Is it the next stage in the evolution of DevOps? Is it just a fancy rebrand for DevOps or SRE? As a veteran of the PaaS (Platform as a Service) discipline about a decade ago, and a DevOps enthusiast at present, I decided to delve into this topic, peel off the hype, and see what it’s about in practice.
October included six new releases and hotfixes, providing new functionality and greater stability to Ninja users. 5.3.6, the largest release, included major new functionality for endpoint management, patch management, and ticketing while reducing setup friction for new customers.
Running network operations today is more complicated than ever before. Networks are growing in size and complexity due to modern network architecture adoption, but organizations are not hiring more staff to compensate for this increased complexity and demand. The prevailing mentality seems to be to do more with what you have.
The InfluxDB platform has evolved a lot over the past decade. But with every innovation we’ve added to the platform, the focus behind our efforts has remained the same: Build cool stuff for people who build cool stuff. What we mean by this is we want to make it incredibly easy for users to build valuable applications with their time series data. We do that by offering a wide range of tools, features, and resources that meet builders on their terms.
