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.
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.
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?