The latest News and Information on Log Management, Log Analytics and related technologies.
This blog post is a companion piece for my talk at https://devopsdaysindia.org. I will discuss the motivations, architecture, and the future of logging in Grafana! Let’s get right down to it. You can see the slides for the talk here.
To stay competitive, companies who want to run an agile business need log analysis to navigate the complex world of Big Data in search of actionable insight. However, scouring through the apparently boundless data lakes to find meaningful info means treading troubled waters when appropriate tools are not employed. Best case scenario, data amounts to terabytes (hence the name “Big Data”), if not petabytes.
Stan Lee believed in the power of strength in numbers, that a group working together can create a force so powerful it’s unstoppable; from “X-Men” to “Avengers”, these teams had a pioneering spirit, heroic work ethics, and group thinking that surpasses individual brainpower almost every time. Today marks that day when the LogDNA superhero team becomes even stronger. I’m excited to announce that we have closed our Series B round of financing.
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.
