Operations | Monitoring | ITSM | DevOps | Cloud

The cloud has long promised limitless scalability and near-perfect uptime. But if you tried to access your Microsoft 365 dashboard or recline your smart bed last week, and got nothing but a spinning icon, you weren’t alone. In the span of 10 days, both Amazon Web Services (AWS) and Microsoft’s Azure Cloud suffered widespread outages that rippled across industries.

Dashboards and alerts can tell you something’s wrong, but they don’t immediately tell you why. A red indicator or synthetic test failure prompts detective work. You flip between dashboards, timestamps, and logs, trying to line up what the check saw with what the system did. Now imagine your monitoring could explain itself by sending traces directly into your OpenTelemetry (OTel) backend.

For a long time, robotic process automation (RPA) was the fastest way to scale repetitive digital work. Bots copied, clicked, and executed rule-based tasks faster than any human. They reduced error rates and delivered early wins for efficiency. Sounds just fine, right? Prepare for a Matrix moment, because the truth is that IT teams built RPA only for predictability. It could follow instructions, but it couldn’t adapt when something unexpected happened.

Over the course of 2025, the Puppet Core team has been committed to developing secure, hardened Puppet code that our customers can rely on. As part of that shift, many Puppet platform components, including Facter, were brought under the Puppet Core model and were moved into private repositories.

Distributed tracing tracks how a single request moves across multiple microservices, helping teams see the entire execution path end to end. In modern architectures where dozens of services interact, it becomes difficult to understand where latency starts, why bottlenecks appear, and which component breaks under load. Traditional monitoring only shows isolated metrics. Distributed tracing connects those dots.