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.

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.

Microservices migrations represent significant investments for organizations seeking greater agility, scalability, and development velocity. Yet without clear metrics to guide the journey and measure outcomes, these initiatives risk delivering technical change without meaningful business impact. Establishing appropriate success measures ensures that migration efforts stay aligned with organizational goals while providing visibility into progress and value delivery.

Moving from monolithic applications to microservices represents a significant architectural transformation. The Strangler Pattern offers a controlled, incremental approach to this migration, enabling organizations to gradually replace functionality while keeping systems operational throughout the transition. This methodology substantially reduces risk compared to complete rewrites, making it an invaluable strategy for organizations with business-critical applications.

Gartner Market Guide for Internal Developer Portals offers “Platform engineering teams are tasked with improving developer experience, ensuring consistent governance, and enabling discovery and access to software development and delivery capabilities. Platform engineering leaders can use internal developer portals to address these challenges.” Organizations with a current Gartner license can check out the report here.