Operations | Monitoring | ITSM | DevOps | Cloud

Datadog and Sentry are two popular tools used for application performance monitoring and observability. Sentry is a dedicated error tracking and performance monitoring service, while Datadog is a comprehensive monitoring platform that unifies logs, metrics and traces. While there are similarities in their capabilities, there are also important differences that organizations should consider when deciding which tool to use.

Consistent performance and continuous improvement: these are the fundamentals we should aspire to in the world of cloud software delivery. We focus on ensuring our systems become more consumable, enjoyable and innovative. We seek to make customers’ lives easier and more productive through incremental achievements, and doing a better job, every day.

When the Gartner Magic Quadrant Report came out in 2022, we did the professional equivalent of a spit take, then cheered wildly. NOT ONLY did they include observability for the first time ever in their newly revamped 2022 Magic Quadrant for APM & Observability, but they also put us in the Leader Quadrant—our debut appearance!

We are thrilled to announce that, for the third consecutive year, Datadog has been named a Leader in the 2023 Gartner® Magic Quadrant™ for APM and Observability. We believe that this placement reflects Datadog’s continued commitment to understanding our customers’ most complex challenges and building products and services that give them the visibility they need into their applications.

Honeycomb has the ability to receive events from applications. These events can take the shape of Honeycomb wide events, OpenTelemetry trace spans, and OpenTelemetry metrics. Because Honeycomb’s backend is very flexible, these OpenTelemetry signals fit in just fine—but sometimes, they have a few quirks. Let’s dive into using metrics the Honeycomb way and cover a few optimizations.

Note: This post is co-authored by Simon Aronsson, Senior Engineering Manager for Canonical Observability Stack. AI/ML is moving beyond the experimentation phase. This involves a shift in the way of operating because productising AI involves many sophisticated processes. Machine learning operations (MLOps) is a new practice that ensures ML workflow automation in a scalable and efficient manner. But how do you make MLOps observable?

Observability is essential for maintaining the performance and reliability of modern software systems. However, the cost associated with attaining and extending observability can quickly escalate in ways that may not even seem apparent at first. We hear from many organizations struggling to tamp down the costs of observability at a time when every dollar spent on technology is scrutinized.

In technical support, ensuring customer satisfaction and quickly resolving issues are of utmost importance. At Honeycomb, we embrace a comprehensive approach by using our own platform—not only for engineering purposes, but to also empower our support team. By utilizing Honeycomb, our support engineers can monitor, troubleshoot, and investigate customer issues with great efficiency.

For a game to provide the best user experience, certain elements come into play. These factors can be hardware components in the user’s computer, like the CPU and GPU, operating system settings, or specific game settings. In fact, if there’s misalignment between these components and a game’s intensity, performance issues can crop up. The most common performance issues in gaming include frame rate drops, input lag, stuttering, rendering issues and network latency.