Ever been a part of a Sprint planning meeting that seemed to last an eternity with no concrete conclusion achieved? Everyone has. And we are here to change that.
This article is all about teaching you how to conduct a Sprint planning meeting that will make your upcoming Sprints more effective, efficient, and, hopefully, less miserable. Let’s start at the very beginning.
Actix is a powerful Actor system for the rust programming language. Actix web is a web framework built on top of Actix.
Actix aims to be:
A neural network, trained to recognize images that include a fire or flames, can make fire-detection systems more reliable and cost-effective. This tutorial shows how to use the newly released Python APIs for Arm NN inference engine to classify images as “Fire” versus “Non-Fire.”
Arm NN is an inference engine for CPUs, GPUs, and NPUs. It executes ML models on-device in order to make predictions based on input data. Arm NN enables efficient translation of existing neural network frameworks, such as TensorFlow Lite, TensorFlow, ONNX, and Caffe, allowing them to run efficiently and without modification across Arm Cortex-A CPUs, Arm Mali GPUs, and Arm Ethos NPUs.
Theoretically, the cloud seems tailor-made for ensuring high availability (HA) and disaster recovery (DR) solutions in mission critical SQL Server deployments. Azure, AWS, and Google have distributed, state-of-the-art data centers throughout the world. They offer a variety of SLAs that can guarantee virtual machine (VM) availability levels of 99.95% and higher.
But deploying SQL Server for HA or DR has always posed a challenge that goes beyond geographic dispersion of data centers and deep levels of hardware redundancy. Configuring your SQL Server for HA or DR involves building a Windows Server Failover Cluster (WSFC) that ensures not only the availability of different machines running SQL Server itself but also — and most importantly — the availability of storage holding the data in which SQL Server is interacting.
Big data continues to become essential to businesses while threats to that data are increasing at an alarming rate. It’s predicted that serious leaks can cost businesses an average of over a million dollars; this doesn’t include any revenue loss or damage to their reputation.
Ethical hackers, also known as white hat hackers, help protect businesses from human error and unscrupulous activities while working to uncover any weaknesses within those servers that can be taken advantage of.
This is already the third article that describes how to test asynchronous operations with the Byteman framework in an application using the Spring framework. The article focuses on how to do such testing with the JUnit 5 library.
The previous article focused on how such testing could be done with the usage of the JUnit 4 library (first article) and Spock framework (second article).
Progressive delivery emerged as a natural response to concerns raised by the idea of “continuous” anything. If teams were going to move faster and release more often, then the surface area for things going wrong would likely be bigger. How could that be managed? And better still, how could risk be reduced while simultaneously increasing the value of moving fast? All of these questions are answered through progressive delivery.
Source de l’article sur DZONE
In order to create a good mobile application, you need to choose a fully-fledged platform and an appropriate SDK toolkit. Both React and Flutter have amazing features allowing you to craft beautiful and multi-functional apps. However, the choice between those two depends on what exactly you are planning to create. Let’s compare the programming possibilities of React and Flutter, their communities, and famous supporters.
Flutter’s history dates back to 2014 when, in pursuit of speed, the Google Chrome team experimented with rendering page content. The purpose of the experiment was to check if it is possible to speed up the rendering when abandoning the traditional layout model. At the same time, the team wanted to get rid of the backward compatibility burden that has accumulated across many years of the web’s existence. The basis for the experiments was the Blink engine, and the result was a 20-fold increase in productivity due to the following changes:
Memory access is so much faster than disk I/O that many of us expect to gain striking performance advantages by merely deploying a distributed in-memory cluster and start reading data from it. However, sometimes we overlook the fact that a network interconnects cluster nodes with our applications, and it can quickly diminish the positive effects of having an in-memory cluster if a lot of data gets transferred continuously over the wire.
With that being said, using proper data access patterns provided by distributed in-memory technologies can negate the effect of the network latency. In this article, we’re using the APIs of Apache Ignite’s in-memory computing platform to see how the performance of our application changes if we put less pressure on the communication channels. The ultimate goal is to be able to deploy horizontally scalable in-memory clusters that can tap into the pool of RAM and CPUs spread across all machines with minimal impact of the network.
The following are some of the most used approaches to handle pluggability on frontend:
Keeping these limitations in mind, let’s have a look at a new approach. First, I will explain it with a simple example and then on a more advanced level.
