Operations | Monitoring | ITSM | DevOps | Cloud

Regulations like the EU’s Cyber Resilience Act (CRA) are top of mind for many in the embedded software world right now, and understandably so. The pressure to comply is real, especially for teams already juggling tight schedules and complex development environments. But as disruptive as these mandates might feel, they also present an opportunity and perhaps a necessary nudge to adopt better habits that can strengthen the software we build, far beyond compliance.

Manufacturers are under pressure to increase efficiency, reduce downtime, and future-proof their factories. Rising costs, global competition, and shifting customer expectations mean that even small inefficiencies can lead to lost revenue or market share. This is difficult while using legacy systems that limit visibility and adaptability. These outdated systems often operate in silos, making it hard to access real-time data, respond to unexpected issues, or scale with modern technologies.

For those deep in the day-to-day operations of a data center, the value of a modern Data Center Infrastructure Management (DCIM) solution is often clear. It helps improve capacity planning, uptime, energy efficiency, and the productivity of people. Data center professionals understand this, and most would love to have DCIM software that simplifies their jobs and helps them deliver better results. But for leadership who controls the budget, the benefits of DCIM software aren’t always so obvious.

Learn how to connect your cloud environments without sacrificing speed, security, or cost efficiency. Businesses aren’t sticking to just one cloud provider anymore. Maybe you’re running apps on AWS, storing data in Google Cloud, and using Azure for AI workloads. It makes sense – each provider has its own strengths. But now you’ve got another challenge: How do you connect them all efficiently without running into performance issues, high costs, or security risks?

Modern applications are often built using microservices: Small, independent components that work together. This makes systems more flexible and scalable, but also harder to monitor. In this guide, we’ll explain what microservice monitoring is, why it’s different from traditional approaches, and how to do it effectively. Whether you’re starting from scratch or improving an existing setup, this article will help you monitor microservices with confidence.

Every day development teams around the world release new software. But what happens to prior releases that are no longer in production? Most organizations save them, typically due to internal policies, external regulations, or simply the fear of losing data. Organizations typically take varied approaches to retaining their prior releases.

I recently wrote an update to my old piece on the cost of observability, on how much you should spend on observability tooling. The answer, of course, is “it’s complicated.” Really, really complicated. Some observability platforms are approaching AWS levels of pricing complexity these days.

If your clients are like most, they use multiple collaboration platforms to drive business and get work done. A common combo is Microsoft Teams and Zoom: over 60% of organizations use both, and together the two have a more than 80% share of the videoconferencing market. More platforms mean more complexity for you to manage — more parameters to watch, more tools to bounce between to keep an eye on things.

Real-time collaboration and virtual meetings have become part of the basic fabric of how work gets done. Because different collaboration platforms do different things really well, most organizations tend to mix and match them to fit their requirements. Teams and Zoom are two of the most common pairings. A study commissioned by Zoom and conducted by the research firm, Metrigy found that 62% of companies use both.

In the world of business, AI is like a superhero - but even superheroes need the right tools to do their job. To make AI truly effective, it’s important to pair it with automated data capture across your operations. Just like us, AI needs a complete picture to make smart decisions and avoid mistakes. Without all the details, it can’t spot patterns, catch defects, or find hidden inefficiencies.