We’re excited to be speaking at and sponsoring KubeCon + CloudNativeCon NA 2018 in Seattle next week and hope we get a chance to hang out. Swing by our booth to check out a new open source project we’ve been working on, and give us some feedback on Grafana and features you’d like to see.
We’re happy to announce the release of advanced analytics for three more ServiceDesk Plus modules: Request transition history, Projects and Knowledge base management. Read on to see how this integration between ServiceDesk Plus and Analytics Plus gives enterprises better visibility into their help desk activity.
Happy December! Back in October, we cohosted a SPOOKY HALLOWEEN meetup with our pals at LaunchDarkly about testing in production. Here’s a review of the talks we saw!
Data science and machine learning have gotten a lot of attention recently, and the ecosystem around these topics is moving fast. One significant trend has been the rise of data science notebooks (including our own here at Sumo Logic): interactive computing environments that allow individuals to rapidly explore, analyze, and prototype against datasets.
One of the biggest KPIs in the DevOps space is monitoring. There are so many tools to help any organization to complete their monitoring picture, but no tool does everything and most organizations use many tools to help complete their monitoring solution. Mashing tools together often creates a problem of its own — the tool sprawl problem.
Sematext provides a single pane of glass and machine learning powered alerts for logs, metrics, traces and digital user experience data. The new Sematext agent is fully Docker Engine and Kubernetes-aware. (Re)written in Go, it has a minimal memory and CPU footprint. It also collects Kubernetes metrics in the most optimal fashion possible.
To gain a better understanding of the adoption and usage of machine data in Europe, Sumo Logic commissioned 451 Research to survey 250 executives across the UK, Sweden, the Netherlands and Germany, and to compare this data with a previous survey of U.S. respondents that were asked the same questions. The research set out to answer a number of questions, including: Is machine data in fact an important source of fuel in the analytics economy?
