Operations | Monitoring | ITSM | DevOps | Cloud

Monitoring doesn't always need to be complex. In this guide, we'll show you how to turn raw logs into usable metrics using a lightweight open-source setup with no ELK stack and no heavy lifting. We'll use Loki, Python, and Telegraf to convert logs into Graphite metrics you can easily monitor or alert on. This is perfect for system admins, DevOps beginners, or anyone curious about building more innovative monitoring pipelines from scratch.

When I first started writing Go software a little over a decade ago, one of the features I found particularly intriguing was the ability to build statically-linked binaries for multiple operating systems and architectures without a lot of headache. This build toolchain feature is widely relied upon by nearly all Go developers, especially when needing to build multi-arch container images destined to be run in a Kubernetes cluster consisting of amd64 and/or arm64 nodes.

In.NET, data access has evolved, but finding the right tool still comes down to control vs. convenience. You have to decide: do you prefer to write every query or move faster with something easier to maintain? Can you manage the boilerplate, or would you rather work with leaner syntax? For many.NET developers, the answers point to one of two popular tools—ADO.NET or Dapper. ADO.NET gives you complete control but with boilerplate and manual overhead.

Frameworks evolve, libraries change, and APIs get rewritten. But your ORM decision? That one sticks—it shapes your architecture, guides how your team writes queries, and affects how painful refactors become later on. In.NET, this choice often narrows to Dapper and NHibernate—two trusted tools with fundamentally different approaches. NHibernate offers deep abstraction, rich mappings, and built-in caching, while Dapper gives you raw speed, total SQL control, and zero overhead.

Security Operations Centers (SOCs) are the command center of an organization’s frontline cybersecurity defenses — responsible for monitoring threats, prioritizing alerts, and orchestrating remediation. However, today’s SOCs are facing an existential crisis: an overwhelming volume of increasingly complex and sophisticated threats combined with a shortage of skilled analysts.

Like the sailor in Coleridge’s “The Rime of the Ancient Mariner,” surrounded by ocean water that he cannot drink, modern organizations contend with similar challenges: data is all around, but it’s not doing them much good (or as much as it could at least). Exploding data volumes have complicated the data management strategies for security and observability teams seeking to contain costs while meeting regulatory and compliance obligations.

If you’re leading a technical organization, you’ll have a critical question when you want to perform uptime monitoring: should wse build and run an uptime monitor ourselves, or pay for a service? While it’s totally possible to build your own basic uptime monitor, you’ll end up costing your business more.

One of the most common questions we receive is about the difference between Aiden (our DevOps Copilot) and general-purpose AI tools like ChatGPT. The key distinction is that Aiden is an agentic AI platform that can directly connect to your DevOps tools and environments, while ChatGPT remains generic without real-world connections. This fundamental difference transforms Aiden from an advisor to an active participant in your DevOps workflows.

With our latest April update, we are setting a new benchmark in incident management excellence. The Signl-Center in our web portal has undergone a major redesign, delivering a superior, more intuitive layout, enhanced tracking of notifications and escalation workflows, and an upgraded incident chat — redefining how operations and maintenance teams coordinate under pressure.