Operations | Monitoring | ITSM | DevOps | Cloud

In any type of software that involves the movement of data or information, there is a pressing need to make the passage of data secure. One way of achieving this is by authentication. You must have experience authenticating API calls or other data streams. In modern systems, where even a small mishap can wreak havoc and you might wake up to a $$$ bill the next day, we should do whatever is within our capacity to secure our systems.

Every millisecond your application takes to respond can decide whether a user stays or leaves. But here’s the catch, you can’t improve what you can’t see. Behind every slow page load, failed API call, or random spike in latency lies a story your application is trying to tell. Application Performance Monitoring (APM) is how you listen to that story.

I'll start this, the third and final entry in my series on APM and Observability, which was originally inspired by my contribution to an APMdigest article, by once again pointing out that APM tools can be built with observability in mind. Many are, in fact. And the ones that aren’t don’t turn into a different type of tool. In my experience, it's more that there's a difference of mindset.

If you’re building high-performance systems, Golang has probably earned a spot in your stack. Its speed, lightweight concurrency, and quick compile times make it ideal for scalable APIs, microservices, and distributed systems. But those same qualities that make Go powerful can make performance monitoring tricky. Goroutines run fast and in parallel, which means a simple CPU or memory graph doesn’t always tell you what’s slowing things down.

Organizations are increasingly looking to build quality and security into their systems and services by design, often by the adoption of frameworks, standards and certifications such as SOC 2 Type 2 audits and ISO/IEC 27001.