Operations | Monitoring | ITSM | DevOps | Cloud

Metrics are fundamental to experimentation for two reasons: They set the basis for evaluating ideas and interventions, and they can suggest where to look next. As such, many teams collect a wide variety of metrics, from application performance data to revenue trends. However, doing so often means manually knitting together data from multiple sources and formats. Even then, data silos can make it challenging to understand the full impact of experimental changes. In this post, we’ll explore.

When external SaaS providers or cloud services degrade or go down, engineers often find themselves wondering if the issue they're encountering is local or more widespread. The answers they find are usually slow to surface, limited in detail, or entirely dependent on the provider's updates. Vendor-controlled status pages and third-party aggregators don’t provide the timely, independent visibility that's necessary to quickly and accurately identify the root cause of slowdowns.

High-performance computing (HPC) environments support some of the most critical workloads in the world—from asset pricing models in financial institutions to molecular simulations in drug discovery. These workloads often span hundreds of thousands of cores, depend on specialized infrastructure such as GPUs, and run for extended periods. As a result, performance and efficiency are critical.

Modern applications depend on dozens of external cloud platforms, APIs, and SaaS services to function. But when those providers experience issues, engineers often spend valuable time asking a basic question: Is the problem with us or with them? Provider-maintained status pages are often slow to update, leaving teams waiting for confirmation while incidents escalate. This delay wastes valuable time, prolongs investigations, and risks customer trust.

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.

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.

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.