Augmented Reality for Industry Repair When augmented reality emerged onto the scene, most people anticipated that it would primarily change consumer industries, like gaming. This idea was seemingly confirmed with the explosive popularity of Pokémon Go, the 2016 AR game that saw gamers capture virtual pokemon out in the real world, using only their phone. But some of the most powerful applications of augmented reality are in industry.

Testing in production is one of the most effective—and risky—ways of testing. The ability to use real-world conditions ensures reliability of tests, as no bugs can appear as a result of misconfigurations of the environment. However, using the same environment as your users also has an obvious downside: any bugs discovered by testing will immediately affect users.

Many of our customers have already embraced mobile transformation and are sending them out in the field to give employees better productivity. For instance, truck drivers now use Android devices for communication and managing their logistics delivery; health organizations use Android tablets to track the location and health status of patients.

Let’s start by stating the obvious: an exception is a problem that occurs during the runtime of a program which disrupts its conventional flow and exception handling is the process of responding to an exception. In Android, not handling an exception will lead to your application crashing and you seeing the dreaded “App keeps stopping” dialog. This makes handling exceptions incredibly important, and let’s face it: no one is going to use an app that continually crashes.

HAProxy Technologies is proud to unveil the 2.7 release of HAProxy Data Plane API. This release was a huge undertaking, and as with the 2.6 release, we focused on extending support for configuration keywords. We are happy to announce that with this release we support all HAProxy configuration keywords in the Data Plane API. Along with enhanced keyword coverage, we’ve added the ability to specify multiple named defaults sections through the API.

A look at what Apache Arrow is, how it works, and some of the companies using it as a critical component in their architecture. Over the past few decades, leveraging big datasets required businesses to perform increasingly complex analysis. Advancements in query performance, analytics, and data storage are largely a result of greater access to memory. Demand, manufacturing process improvements, and technological advances all contributed to cheaper memory.

A little over a year ago, we released Grafana Machine Learning, enabling Grafana Cloud Pro and Advanced users to easily view forecasts of their time series. We recently enhanced Grafana Machine Learning with Outlier Detection, which allows you to monitor a group of similar things, such as load-balanced pods in Kubernetes, and get alerted when something starts behaving differently than its peers.