In recent weeks, the coronavirus pandemic has forced millions of people around the world to suddenly work from home. For many, remote work had been an occasional experience; for others, it was altogether unfamiliar. Now, working from home is the “new normal” for many of us, at least for the short-term. If you are new to remote working, it can feel challenging to adjust as you find a new rhythm for connecting with others and managing the workday.

When done well, application performance monitoring (APM) is a magical and irreplaceable tool. However, over the years, enterprises have sometimes paid dearly to implement it. APM’s task seems simple enough—replace emotional user anecdotes about application performance with quantifiable, actionable data. But the devil can certainly be in the details with APM.

Page load time and response time are key metrics to monitor, and they can give you an in-depth understanding of how your website is performing. However, the difference between page load time and response time isn’t immediately obvious, and neither are the benefits of tracking them independently.

In the modern digital ecosystem, every user activity, system error, application transaction, and network packet movement can be tracked using logs. This level of visibility into systems, networks, and applications is useful for troubleshooting bottlenecks, analyzing past trends, and predicting future events. However, monitoring various cloud-based and on-premises resources becomes complex in the absence of proper log aggregation tools.

With Google’s new support for Windows containers on Google Kubernetes Engine, you might be eager to start bringing your .NET-compatible Docker workloads to Google Cloud Platform. JFrog is ready to help you hop aboard. In fact, Artifactory’s bags are already packed.

There’s a lot going on in the world right now. The current healthcare crisis is making a lot of companies reconsider their next moves and forcing them to radically rethink how they operate and embrace technological investment in their warehouses and distribution centers. Also, Windows-based handheld computers that have long been found in the hands of warehouse workers for picking, packing, loading, inventory, etc., are nearing the end of life.

Kubernetes governance may sound dull. But, if you’re an enterprise, it’s a critical part of what you must figure out to be production-ready at scale. When standardizing on-demand services for your dev teams — a DevOps best practice — you must ensure that groups deploying Kubernetes clusters follow certain rules, a process that is typically automated via policy management.

As Elasticsearch users are pushing the limits of how much data they can store on an Elasticsearch node, they sometimes run out of heap memory before running out of disk space. This is a frustrating problem for these users, as fitting as much data per node as possible is often important to reduce costs. But why does Elasticsearch need heap memory to store data? Why doesn't it only need disk space?

TL;DR: yes, API Gateway can replace what a Load Balancer would usually provide, with a simpler interface and many more features on top of it. The downside is that it doesn’t come cheap. Load balancers have been one of the most common ways to expose a backend API to the public or even to an internal/private audience. API Gateways seem to provide the same functionality: map and connect HTTP requests to a backend service. So, are they the same or are there any differences?

After StatsD and Graphite weren’t able to meet their needs for metrics and monitoring, engineers at SoundCloud developed the open source event monitoring and alerting tool, Prometheus. Because it’s easy to deploy and get started with -- and on the surface seems free -- it’s become a popular part of many DevOps teams' observability stack.