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Both Grafana Tempo and Jaeger are distributed tracing tools designed for modern microservice architectures. Jaeger, released as an open-source project by Uber in 2015, has matured into a graduated CNCF project. Tempo, announced by Grafana Labs in October 2020, is a newer entrant focused on high-volume tracing with a unique storage architecture. Before comparing these tools in detail, let's quickly review what distributed tracing is and why it matters.

When something breaks in production, logs are usually where you start. They help you figure out what happened, where, and why. But with microservices architecture, logging isn't simple anymore. In a traditional monolithic application, logs live in one place. With microservices, they're scattered across multiple services, containers, and sometimes even data centers. What used to be a simple grep command now feels like solving a mystery without most of the clues.

No matter where or how you work, we all have the same goal when an incident occurs: to get it resolved effectively and efficiently—and as quickly as possible. However, the way we achieve that goal isn’t always the same. We understand that different organizations operate differently, so you need flexibility from your IRM tooling.

With the 3.0 release, Prometheus firmly established itself as the leading metrics database for OpenTelemetry. A lot of work has gone into integrating the two open source projects, including a major Prometheus enhancement we’re really excited about: resource attribute promotion.

With Cisco Prime Infrastructure approaching its EOL and EOS in September 2025, network administrators are at a crossroads. The transition away from this long-standing network configuration management tool necessitates a strategic evaluation of alternatives that align with organizational needs and budgets.

In 2025, the role of compliance officers and risk managers has never been more complex—or more critical. New regulatory requirements, AI-generated content, and increasingly sophisticated cyber threats have dramatically raised the stakes. Here is just a small set of the pressing challenges facing compliance monitoring officials this year.

The rise of artificial intelligence has revolutionized the way businesses create and manage digital content. As of 2024, over 45% of marketing teams are actively using generative AI tools like ChatGPT, Claude, and Jasper to create website copy, blog posts, product descriptions, and more (Salesforce). Meanwhile, Gartner predicts that by 2026, 80% of content on the internet will be AI-generated.

For decades, enterprise IT teams have operated in a state of controlled chaos. Pressured to keep digital lights on, these teams have spent far too much time buried in logs, swatting away alerts, and fighting fires one incident at a time. The familiar mantra—“do more with less”—has translated into a culture of reactive operations, where innovation takes a backseat to survival.

Mobile developers are increasingly adopting Kotlin Multiplatform to share business logic across iOS and Android. While Kotlin Multiplatform reduces duplication of code-writing efforts, it also introduces blind spots. Developers often lack real-time visibility into how shared code performs across platforms, making it harder to troubleshoot issues and monitor user experience.

Finding the right integrations, libraries, and open source tooling to extend a product has long been a challenge for developers. While Datadog has a vast offering of monitoring and observability solutions, many teams need to customize their setup in some way—whether by extending the Datadog Agent, integrating with third-party services, or using SDKs to interact with the Datadog API.