Operations | Monitoring | ITSM | DevOps | Cloud

In Part 1, we explored how aligning network visibility with IT service context empowers faster, smarter incident response. But what does this actually look like? Here in Part 2, we’ll go deeper into the challenges of traditional monitoring approaches, and how teams should look to move from fragmented alerts to unified insights – because when ITOps and NetOps can both see the “what” & “why” of the problem, actions become instinct.

Trends in tech come and go, but certain underlying primitives stick around forever. In software, two such primitives are virtual machines and containers. Virtualization paved the way for the cloud to become massive. Data centers would likely never have been commercially viable without it. While still relatively new, containerization has already made a serious mark on the software engineering world.

What if you were to tell Kubernetes Monitoring what you wanted, and the system configured collectors based on your choices? We wondered that as well—wondered enough to create Alloy Operator and its Helm chart for version 3.0 of the Kubernetes Monitoring Helm chart. We’re excited to share that the new Kubernetes Monitoring Helm chart is now available, and it introduces a dynamic way of setting up your telemetry data collection with Alloy Operator.

Sometimes your API call takes a few seconds longer than expected. Or users start reporting slow page loads. One of the most common reasons? Network latency.

If your users are spread across the globe but your servers are sitting in Virginia, you’ll probably hear complaints about slow load times, especially from places like Australia. CDNs fix this by caching static assets closer to where your users are. Azure CDN does exactly that, and it fits well if you're already using Azure services. You can hook it up to Blob Storage, App Services, or your origin. This guide covers how to set it up, what to expect, and how to know it’s working.

Time series data, such as IoT sensor readings or stock market ticks, flow in fast, often at a rate of millions of points per second. Querying this data, especially years of historical records, can be slow and painful if using a nonspecialized database rather than a time series database like InfluxDB.

AIOps platforms have gained traction because teams are dealing with more alerts, data, and monitoring tools than they can realistically manage. The goal isn’t to replace people, but to reduce the manual work that comes with sorting through alerts, figuring out what’s actually wrong, and figuring out who should respond. Some tools focus on event correlation. Others lean into automation, anomaly detection, or service-level visibility.

In a traditional IT environment, the service catalog has long functioned as a directory—an online menu of things users can ask IT for. Need a new laptop? Submit a ticket. Need Salesforce access? File a request. Every need, every problem, every question gets funneled into a form, queued up for manual processing, and eventually (and hopefully) resolved. But this static, ticket-heavy model can’t keep up with the pace of business today. Employees expect seamless, self-service experiences.

As developers, we often fall into common pitfalls that impact the performance, security, and scalability of our applications. From neglecting data validation to overengineering, and from ignoring async/await to mishandling resource disposal, even experienced C# developers can make these mistakes. In this post, I've gathered some of the most frequent issues developers encounter in C# and how to avoid them with practical solutions.

Hyperview stands out with its transparent and flexible subscription-based pricing. Being a cloud-based solution, it eliminates hefty upfront costs often associated with traditional DCIM software. Updates and upgrades are rolled out seamlessly with zero downtime, and there are no hidden fees, making it highly budget-friendly for businesses looking to control long-term costs.