Operations | Monitoring | ITSM | DevOps | Cloud

by Sam Fell, VP, Product Marketing, Observability, Sumo Logic I love technology, and I’m thrilled to work in a profession where I’m steeped in it! In my career as a developer, consultant and marketeer I've learned it’s not “the cool new tech stack” that helps win the day.

A little over a year ago, we announced a partnership with DigitalOcean to make it easier for users to add log management and monitoring for applications running in DigitalOcean Droplets (VMs). Since then, we’ve created a DigitalOcean Marketplace Listing, provided a direct link to the signup page, and shared many ideas on how to use SolarWinds® Papertrail™ to troubleshoot and optimize applications running in DigitalOcean Droplets.

Shopify downtime can be a real risk to your business. It can cause you unprecedented losses. For example, it can prevent clients from accessing your ecommerce store. Anytime you experience situations like slow browsing or outage, it’s crucial to act swiftly to troubleshoot the issues and fix them. How should you go about it? In this article, we will.

One of the first things you’ll learn when you start managing application performance in Kubernetes is that doing so is, in a word, complicated. No matter how well you’ve mastered performance monitoring for conventional applications, it’s easy to find yourself lost inside a Kubernetes cluster.

In the present age of cloud-native everything, it can be easy to forget that some applications still run on-premises. But they do and managing the performance of on-premises apps is just as important as monitoring those that run in the cloud. With that reality in mind, here’s a primer on how to approach on-premises application performance monitoring as part of a broader cloud-native performance optimization strategy.

For DevOps teams, delivering quality software has long required reconciling a major tension: In a perfect world, you’d catch every issue in each new release of your application before you deployed the release into production. But in the real world, doing so is tricky, not least because it’s hard to collect data about application performance before the application is actually deployed.

Computer science is crucial to our lives today, and programming languages play a fundamental role. These languages act as a programmer’s toolbox. However, choosing a language can be challenging, especially when deciding between the two most popular options. These options include Java and Python. Java and Python are widely used as general-purpose programming languages for desktop and web applications. There are many similarities when comparing the two, but there are also significant differences.

The phone rings. Your email pings. Your marketing team just told you about a flood of messages on social media and through live chat that there’s a service outage. You thought your Monday morning would be calm and relaxed since people are just returning from the weekend. How do you start researching all of these incoming tickets? How do you know which ones to handle first? Is this just a hardware failure, or are you about to embark on a security incident investigation like Log4j?

By now, most of us know that migrating workloads to the cloud isn’t like simply moving software from one server to another—especially in a complex enterprise infrastructure with many interdependent services and components. There are a lot of things that can go wrong: technical public cloud provisioning issues, security and compliance challenges, lack of cloud skills, wrong public cloud service provider (CSP) selection, unexpected costs, and more.