Operations | Monitoring | ITSM | DevOps | Cloud

Data integrity in a database is the backbone of accurate decision-making, regulatory compliance, and organizational security. Without it, even the most advanced analytics or AI models are built on shaky ground. Imagine going through your database and noticing that the numbers in your sales report are inconsistent, financial data tells three different stories, and some customer records are missing. This mismatch can compromise business decisions and lead to missed opportunities.

For most teams, the slowest part of delivery isn’t writing code, it’s everything that happens after: automated tests, manual reviews, bug fixes, final approvals, and the long wait for deployment. The longer these phases run, the more expensive and painful late fixes become. As AI makes it easier to generate code at scale, those bottlenecks only get bigger.

Most operations teams trust their green dashboards. If the internal monitoring says everything is healthy, the app must be fine, right? But as the Internet keeps proving, what’s green inside the firewall can look red for customers outside of it. Sometimes, a single change in how web traffic moves can suddenly slow logins, disrupt websites, or hurt business results, even if everything looks fine inside.

The 2025 DORA State of AI-Assisted Software Development Report delivers a critical insight for technology leaders: AI is fundamentally an amplifier, not a solution. It magnifies the strengths of high-performing organizations with robust observability while exposing the dysfunctions of struggling ones. For organizations that have rushed to adopt AI coding assistants all while expecting immediate productivity gains, this finding demands a strategic pivot.

Argo CD has built a number of Generators to support various scenarios that developers need when using Argo CD and Kubernetes. In this post, I’ll be discussing the Pull Request Generator. A Pull Request Generator is an Argo CD Application Set deployment type that is configured to “watch” a Git repository for Pull Requests (PRs). Whenever a new PR is submitted that matches the specified filter, Argo CD applies the manifests from the referenced repository and path.

Companies today face growing pressure to manage and analyze massive flows of time series data, from IoT sensors to cloud-native infrastructure. Storing this information is relatively straightforward. The greater obstacle is keeping it useful and consistent while balancing a wide range of tools and modern technology platforms that continue to evolve.

Over the years, we’ve seen our community embrace Grafana k6 browser as a key component of their frontend testing strategies. By helping collect frontend web vitals, capture custom metrics, and simulate user actions like clicking buttons or completing forms, the module offers teams a deeper understanding of performance and availability from their end users’ point of view.

Your most skilled engineers shouldn’t be spending nights and weekends piecing together root causes of outages. Yet many organizations still rely on manual incident response across sprawling hybrid and multi-cloud environments. The result: slower resolution times, frustrated customers and lost revenue that can reach up to $1 million per hour according to IDC. At LogicMonitor, we believe the answer isn’t just better monitoring. It is systems that can heal themselves.

5 minutes. That’s how much downtime some of the world’s largest enterprises will tolerate. For most organizations, five nines (99.999%) of availability sounds like a pipedream. But the trick to increasing availability isn’t massive infrastructure spending or complex system redesigns. All it takes are three key practices that any team can adopt and implement. In this post, we’ll present these practices and how we implement them at Gremlin.

We're obviously a little biased, but we believe we have one of the best website monitoring tools on the market today, leading in features compared to our competitors. We've already tried a variety of marketing techniques to promote our service, but none really had the impact we were looking for. Maybe we're better at actually building good software than we are at marketing it? Or are we trying what everyone else is also doing, thus making it all harder?