Operations | Monitoring | ITSM | DevOps | Cloud

It all began when I spun up an AWS t4g.small burstable instance for a side project. Nothing unusual just another day in the cloud. But the moment I connected through SSH, something caught my eye. The system greeted me with a temperature reading of -273.5°C. Wait… what? That’s 0 Kelvin, the point where atomic motion completely stops. In other words, absolute zero , a state that’s theoretically impossible for anything to operate in.

All the things you may have missed that will make your incident management smarter, faster, and simply easier. We ship updates every week because we want you to get the most out of FireHydrant. But we also know it's hard to stay up to date and read every week's changelog (even though we know reading changelogs is the highlight of your week ).

Many organizations rely on OpenTelemetry (OTel) to standardize observability across distributed systems. These organizations are at varying stages of adoption and are implementing OTel in complex environments with diverse configurations. To support this range of use cases, Datadog offers many ways to use OpenTelemetry with Datadog.

Historically, developers have relied on languages like C and C++ for explicit control over memory allocation and deallocation. This approach can yield very low overhead and tight control over performance, but it also increases complexity and risk (e.g., memory leaks, dangling pointers, and double frees). This often results in runtime issues that are difficult to diagnose, which can become a drag on team velocity.

A synthetic test is only useful if you can trust what it’s telling you. When one fails, the reason may not be obvious. Was the application updated? Did the test change? Or both? As more people contribute and refine the same test, it becomes harder to understand what changed or restore a working version. Without clear visibility into those updates, teams can spend more time tracking down the cause of a failure than resolving it.

Modern security operations teams face an overwhelming challenge: a rapidly growing volume of logs, alerts, and telemetry from cloud services, on-premises infrastructure, and third-party security tools. Traditional SIEM platforms often struggle to scale cost-effectively and provide the agility needed for advanced analytics and threat hunting.

SharePoint is the backbone of internal collaboration for countless organizations. It hosts documents, drives workflows, powers intranets, and underpins team communication across departments. But when it slows down—or worse, goes dark—productivity grinds to a halt. The problem is that most monitoring approaches treat SharePoint like a static website. They check availability, not experience.

In 2018, attackers gained access to Tesla’s AWS cloud environment through an unprotected Kubernetes console (admin console). Because it lacked proper authentication, the hackers could see and control cluster resources. Once inside, they deployed new pods running cryptocurrency mining software, using Tesla’s compute power for profit. During the breach, the attackers also uncovered credentials stored in the cluster.

As of 2025, more than 110 million Mexicans are online, putting digital‐access penetration at roughly 83% of the population. Mexico is already one of Latin America’s anchor markets, leading the region in startup momentum, cloud adoption, and cross-border digital trade. A few days ago, CloudHQ announced a $4.6B investment in Mexico to open multiple datacenters. Yet even with this scale, service quality still varies dramatically across cities, states, and ISPs.

When every second of downtime can mean lost revenue and frustrated customers, leading businesses can’t afford to leave performance to chance. That’s why leading companies are turning to Application Performance Monitoring (APM) tools like Atatus, a Datadog alternative to keep their applications healthy, detect issues before customers do, and achieve higher uptime than ever. But how exactly are they doing it?