Grafana is an open-source platform for data visualization, monitoring, and analysis. It's designed around providing context-rich visualizations, mainly though graphs but also supports other ways to present data through pluggable panel architecture. Every dashboard is versatile and custom-buildable for specific projects of software development or business requirement. Grafana’s beautiful dashboards are one of the reasons Grafana is so popular with users.
The DevOps infinity loop is a visualization that everyone reading this article is likely familiar with, and one that comfortably predates serverless computing. Yet despite the peculiarities of developing within the serverless paradigm, it’s still what guides our approach to CICD. But while the zoomed-out view might be the same, when it comes to adjusting for serverless, the devil is in the details.
欢迎! [Huānyíng] In Mandarin, this means “welcome,” the first Chinese phrase I ever learned as a Mandarin Language Minor in college. It took me two weeks to understand the tonal variations, one week to memorize and properly execute the written stroke pattern, and another week to hone the ability to say it with confidence to my teacher (aka 老师 [Lǎoshī]).
The Deep Learning Toolkit (DLTK) was launched at .conf19 with the intention of helping customers leverage additional Deep Learning frameworks as part of their machine learning workflows. The app ships with four separate containers: Tensorflow 2.0 - CPU, Tensorflow 2.0 GPU, Pytorch and SpaCy. All of the containers provide a base install of Jupyter Lab & Tensorboard to help customers develop and create neural nets or custom algorithms.
Workload management is a powerful Splunk Enterprise feature that allows you to assign system resources to Splunk workloads based on business priorities. In this blog, I will describe four best practices for using workload management. If you want to refresh your knowledge about this feature or use cases that it solves, please read through our recent series of workload management blogs — part 1, part 2, and part 3.
In this blog post, we discuss using Telegraf as your core metrics collection platform with the Splunk App for Infrastructure (SAI) version 2.0, the latest version of Splunk’s infrastructure monitoring app that was recently announced at Splunk .conf19. This blog post assumes you already have some familiarity with Telegraf and Splunk. We provided steps and examples to make sense of everything along the way, and there are also links to resources for more advanced workflows and considerations.
When making investments in our tech stack, we tend to have doubts about companies that don’t use their own products and services. At Elastic, we deploy the full suite of our technology across the enterprise. We do so because our technology not only works, but it makes us more efficient and flexible on so many levels. And it can do the same for you and your business, too.
If you’re logging onto a service or running remote command line operations over a network link via the Secure Shell (SSH) protocol, the last thing you need is for your session to be cut off by a faulty connection. This scenario is all too common – but for Linux users, the Screen utility can prevent it from occurring.
Measuring the maturity of your DevOps team might sound difficult, but it isn’t at all. Simple key performance indicators (KPIs), such as the deployment success rate or mean time between failure, give a good indication of the maturity of your DevOps team. By “mature,” I mean that your team consistently and smoothly operates at a high level and can deploy several times a day with very little risk.
If you are a developer who uses containers, chances are you and your team have heard about Kubernetes. At its core, Kubernetes is a container operating system for the web, but has grown to be much more. Sure, Kubernetes can manage your containers, network traffic, and bring up a crashed ad, but it has also become a widely adopted platform with a growing community.
