Operations | Monitoring | ITSM | DevOps | Cloud

Learn how data observability helps your business catch issues early, ensuring accurate insights, smarter decisions, and smoother growth.

The way we manage, qualify, and utilize our data is constantly tested. With the amount of information we have at our disposal, managing and ensuring data quality has become a strategic lever for companies striving for excellence. How can we ensure our data management is flawless and the data quality on which we base our decisions is optimal? This is where data observability becomes an essential component.

When you build software, its terminology, concepts and relationship between them is quite obvious to you, when you’re starting to use software built by someone else – might not be so much so. In this blog post I tried to cover most important Coroot concepts and terminology – reading it will hopefully help you to understand Coroot much better if you’re just starting up with it.

Now that AI has revolutionized IT forever, what are its implication on IT observability? Typically, IT operations, SREs, and DevOps professionals use IT observability to gain a holistic view of their IT infrastructure. In that pursuit, they used AIOps in several ways. Now, AI has helped IT observability with better anomaly detection, faster root cause analysis, and proactively identifying opportunities to dynamically scale IT to ensure uptime, performance, and security.

Datadog has established itself as one of the leading solutions for monitoring, logging, and analytics. But with the increasing number of alternatives available, many businesses are asking, "Is Datadog worth the price?" This article breaks down Datadog's pricing structure, the value of its features, and compares it to competitive alternatives. By the end, you'll have a clear understanding of whether Datadog is the right fit for your business.

As a software engineer, I’m always evaluating tools and technologies that can optimize my workflow. Developer productivity isn’t just about writing more code—it’s about reducing friction, whether that’s context-switching, making repetitive edits, or understanding unfamiliar parts of a codebase. That’s where GitHub Copilot comes in: making tasks that once felt monotonous or time-consuming into faster, more intuitive processes.

Ready to migrate your observability platform? Here’s what you need to know to make the process smooth and set your team up for success.

Learn how unified observability helps you track system health, improve performance, and quickly resolve issues across your environment.

In structured logging, log messages are broken down into key-value pairs, making it easier to search, filter, and analyze logs. This is in contrast to traditional logging, which usually consists of unstructured text that is difficult to parse and analyze.

The Collector is one of many tools that the OpenTelemetry project provides end users to use in their observability journey. It is a powerful mechanism that can help you collect telemetry in your infrastructure and it is a key component of a telemetry pipeline. The Collector helps you better understand what your systems are doing—but who watches the Collector? Let’s look at how we can understand the Collector by looking at all the signals it’s emitting.