Operations | Monitoring | ITSM | DevOps | Cloud

AI coding assistants like Claude Code are becoming core parts of modern development workflows. But as with any powerful tool, the question quickly arises: how do we measure and monitor its usage? Without proper visibility, it’s hard to understand adoption, performance, and the real value Claude brings to engineering teams. For leaders and platform engineers, that lack of observability can mean flying blind when it comes to understanding ROI, productivity gains, or system reliability.

Microsoft Azure Data Factory is a cloud-based data integration service provided by Microsoft Azure. It enables you to create, manage, and automate data workflows that move and transform data from different sources to various destinations. Essentially, ADF allows you to design, orchestrate, and manage data pipelines, making it easier to work with large volumes of data across on-premises and cloud environments.

When debugging containerized applications in Kubernetes, kubectl logs serves as your primary command-line tool for accessing container logs directly. Understanding how to effectively retrieve, filter, and analyze logs becomes essential for maintaining application health and resolving issues quickly, especially in multi-container environments where correlation across services can make or break your troubleshooting efforts.

Node.js has become the go-to runtime for building modern, high-performance applications. Its event-driven, non-blocking I/O model makes it particularly well-suited for apps that demand speed and scalability, such as real-time chats, gaming backends, streaming platforms, fintech dashboards, and e-commerce systems. It’s no surprise that some of the world’s largest companies like Netflix, PayPal, LinkedIn, Walmart rely on Node.js to deliver services at scale.

When your Python app starts slowing down, maybe queries are taking longer, memory keeps creeping up, or API calls are lagging—basic server metrics won’t tell you why. You need to see what’s happening inside the application itself. That’s the role of Application Performance Monitoring (APM). It gives you a breakdown of database queries, external API calls, memory usage, error rates, and more, so you can connect the dots between code and performance.

Awareness of data sovereignty is increasing within organizations. Geo-political situations and recent news stories are causing many to formally evaluate their data management strategies and policies. This means that organizations are also looking at the tools and platforms they use to run and maintain key IT infrastructure and undertake tasks such as monitoring and management. SaaS and cloud first/only tooling can often present data sovereignty challenges and complications.