Operations | Monitoring | ITSM | DevOps | Cloud

I recently wrote about a project I created on AWS Aurora PostgreSQL where I'm capturing APRS data from a radio. I focused on the ease of use, getting a database, some Lambda Functions, and a few schedulers working together with a web page. It was easy. However, I'd like to focus on a slightly different area now, performance.

At MWC Barcelona 2026, coRAN Labs and Canonical are presenting a working demonstration of a cloud-native, quantum-safe telecom platform for 5G and beyond 5G networks. This is not a conceptual exercise. It is a full 5G System (5GS) deployment with post-quantum cryptography embedded across the stack – from radio access to core, from transport interfaces to orchestration and public key infrastructure (PKI).

This post is also available in German and in French. The 2026 AI landscape has shifted from "Can we build it?" to "How much will it cost to run it?" For CTOs and engineering leaders, the challenge is no longer just model performance: it is the underlying infrastructure sprawl that silently erodes margins. When AI workloads scale, they often inherit the inefficiencies of legacy cloud models: over-provisioned instances, fragmented data pipelines, and a lack of unified context.

Learn what causes high multicloud latency, and how you can reduce it with a few simple methods – no hardware deployment required. Latency is usually one of those problems that shows up before anyone has time to go looking for it – and troubleshooting it can feel like you’re aiming for a moving target.

Think about the last time your team shipped something impressive. It was probably on time, clean code, and had great metrics. And yet somewhere along the way, the business priorities had shifted, and what the team delivered was no longer the top priority. The work was solid, but the direction just wasn't quite right anymore. This is usually what happens when engineers are disconnected from business context.

Picture this: it's 2am, your pager goes off, and you're staring at a production database that's on fire. You know exactly what's wrong. You know exactly how to fix it. But you can't touch anything because you're waiting on someone to approve your access request. Meanwhile, your customers are down, your SLAs are bleeding out, and you're refreshing Slack hoping someone in security is awake to click "approve." This is the incident response tax that too many teams pay.

Is Kubernetes complexity killing your team’s velocity? Compare Docker vs. Kubernetes in 2026 and discover how to get production-grade orchestration with the "Git Push" simplicity of Docker.

Kubernetes is increasingly the standard for deploying, running, and maintaining cloud-native applications running in containers. Kubernetes (K8s) automates most container management tasks, empowering engineers to manage high-performing, modern applications at scale. Meanwhile, surveys from VMware and Gartner reveal that insufficient Kubernetes expertise prevents many organizations from fully adopting containerization. Understanding how Kubernetes components work removes this barrier.

Most database problems get blamed on queries, schemas, or infrastructure, and rarely on SQL Server ODBC drivers. Fair enough, those components often break. But in many cases, the real culprit is the connection layer itself. Poor drivers can lead to security gaps, performance hits, cloud connection struggles, Unicode issues, or incompatibility across platforms and modern SQL Server versions.

Compare 9 incident management tools: PagerDuty, Opsgenie, Incident.io, Rootly, FireHydrant, BetterStack, Grafana OnCall, Squadcast, and Last9. Features, pricing, and which fits your team. Product Marketing Manager.