The rapid pace of updates and upgrades to operating systems, software frameworks, libraries, programming language versions – a boon to the future of fast-paced software development, has also come to slightly bite us in the back because of having to manage these very many dependencies with their different versions across different environments.

Over the last year, we have witnessed a shift in engineering working habits. COVID-19 forced many of us into lockdown. Instead of working from the office, coffee shops, and airport lounges, I found myself mostly working out of my (hastily built) home office. For many of us, this meant shifting back to a workstation over a trusty laptop. Not surprisingly, this did nothing to abate the heated discussion over which computers and operating systems are best for developing software.

Each day, more and more companies consider opting for cloud-based solutions, and they almost always end up adopting them to some extent. While the increasing popularity of cloud services may be a significant factor in accelerating the adoption rate of cloud-based solutions, some individuals remain skeptical of migrating their applications to the cloud due to unfamiliar territory.

5G is in the process of transforming communications technology, enabling never-before-seen data transfer speeds and high-performance remote computing capabilities.

Microservices have become a popular way to architect applications, particularly those that compose functionality from a variety of loosely coupled systems and services. While there are a variety of frameworks and tools for implementing a microservice architecture, it isn’t always clear how to expose native code like C or C++ code within a wider microservice system. That’s where HydraExpress comes in.

In this first section, I’ll provide a quick overview of the business case and the tools you can use to create a Kubernetes ingress API gateway. If you’re already familiar, you could skip ahead to the tutorial section. Digital transformation has led to a high velocity of data moving through APIs to applications and devices. Companies with legacy infrastructures are experiencing inconsistencies, failures and increased costs. And most importantly, dissatisfied customers.

As a team we have spent many years troubleshooting performance problems in production systems. Applications have gotten so complex you need a standard methodology to understand performance. Fortunately right now there are a couple of common frameworks we can borrow from: Despite using different acronyms and terms, they fortunately are all different ways of describing the same thing.

The project used in this article is experimental and changes a lot between commits. Use at your own discretion .