Operations | Monitoring | ITSM | DevOps | Cloud

Traditional IT process automation has always promised faster, more efficient operations. But for years, it’s been largely synonymous with service desk workflows: password resets, access requests, and the like. Those are important, no doubt. But limiting automation to the service desk is like only automating the assembly line in a factory while leaving the rest of the production floor manual.

Docker Swarm's logging model shifts from individual container logs to service-level aggregation. The docker service logs command batch-retrieves logs present at the time of execution, pulling data from all containers that belong to a service across your cluster. This approach gives you a unified view of distributed applications, but it comes with its patterns and considerations for effective observability.

When your Go application moves beyond development, you need structured logging that persists. Writing logs to files gives you the control and reliability that stdout can't match, especially when you're debugging production issues or need to meet compliance requirements. This blog walks through the practical approaches, from Go's standard library to structured logging with popular packages.

When someone leaves your organization, the first step IT usually takes is to disable their Microsoft 365 account. But have you ever stopped to ask: The answer might surprise you. If you’re not actively managing this, Microsoft will automatically delete that data — often in as little as 30 days. This post explains exactly what gets deleted (and when), why this is a problem, and what you can do to protect that data — without paying for unnecessary licenses.

It’s only a matter of time. OpenAI CEO Sam Altman said in 2024 that he thought this could be achieved by the end of 2026. Personally, I feel this is a little optimistic; however, based on the evidence I’ve seen, it won’t be long after that. Consider Telegram: a global messaging giant with just 30 employees, already achieving a remarkable $1 billion in revenue. Or Midjourney, revolutionizing creative industries with only 40 employees and generating an impressive $500 million.

With the rise of AI and machine learning comes data. Lots of it. For organizations today, AI is radically changing the way data is accessed, maintained and operationalized. For heads of architecture and development teams, it offers opportunity and responsibility.

Google Cloud released Google Kubernetes Engine (GKE) as a commercial version of native Kubernetes (K8s). GKE promises a user-friendly, reliable, and cost-effective service. Yet calculating GKE costs can be daunting, including understanding what you’re paying for and maximizing your return on investment. In this GKE pricing guide, we’ll discuss how GKE pricing works, what it costs, and more.