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.
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.
Blue Monday is here! Yes, we’ve all heard about and experienced the Monday blues, but what is Blue Monday?
We had a busy 2019 with a substantial number of Amazon Web Service (AWS) integrations into Site24x7, each providing a seamless monitoring experience for our customers. The increasing number of paid AWS monitors is proof that we enhanced the monitoring expectations in 2019. In case you missed any updates about the AWS monitoring platform, here's a year-end review.
Let’s face it—distributed streaming is an exciting technology that can be leveraged in many ways. Use cases include messaging, log aggregation, distributed tracing, and event sourcing, among others. Distributed streaming can result in significant benefits for companies that choose to use it, but, when not implemented correctly, it can initiate a frustrating technical debt cycle. How do you know if you’re properly implementing Kafka in your environment?
Congratulations!! After 3 months of hard work, your developer has done an incredible job and your brand new web application is ready to be put online. But wait a minute… Who is going to put it online? Try to ask your developer to do it - there is a good chance that he can’t. Why? Deploying applications is not necessarily a part of their qualifications. And even if he is able to, who's going to make sure it's always available and scale as your business grows?
Don’t let the title mislead you, we love DevOps here at Cycle. Without proper DevOps processes, building and scaling cloud-based applications can become a nightmare for maintainability. A proper DevOps plan brings together an organization’s developers, QA support, and operations teams to pursue the goal of delivering software more predictably. An admirable goal for any team and something that can be immensely helpful for even small teams to become more efficient.
