Operations | Monitoring | ITSM | DevOps | Cloud

Moving quickly is essential for modern engineering teams, but speed without guardrails can introduce hidden risks in testing. As organizations scale, teams often define and apply coverage standards inconsistently across services and repositories. What qualifies as “acceptable coverage” in one project may be completely different in another. Without automated enforcement, untested code can slip through reviews.

High-performance computing (HPC) clusters rely on fast, reliable shared storage so that expensive CPU cores and GPUs aren’t left idle waiting for data. When bandwidth or I/O bottlenecks emerge, your workloads can slow down as your cluster spends more time on blocked reads, metadata lookups, and sync operations than actually computing.

Threat detection and runtime posture monitoring are related but different jobs. Security teams already rely on Datadog Workload Protection to detect threats in real time across hosts and containers. But the actions that lead to those detections (file manipulation, process execution, network calls, or kernel activity) can be indicative of compromise or simply of risky behavior—like running compilers in production containers.

Product managers (PMs) need to make fast, confident decisions about what to build, fix, and improve based on user behavior within their application. But in practice, collecting the user insights they require is rarely straightforward. Recent updates to Datadog Product Analytics address this challenge. Product Analytics adds structure to autocaptured data and makes analysis easier to interpret, reuse, and share, helping PMs move from questions to answers without relying on SQL or engineering.

Organizations are increasingly using agentic AI applications powered by large language models (LLMs) to automate analysis, decision-making, and operational workflows. As these AI agents take on more responsibility, they gain access to internal tools and services and can interact with them in unintended ways.

Event-driven systems are great at decoupling services, but they also make incidents harder to untangle. A single user request can turn into dozens (or thousands) of messages, multiple consumers, retries, and delayed acknowledgments. If your tracing only tells you that a message was sent or received, you still have to guess which upstream request produced the message, whether a batch publish fanned out cleanly, and where queue time is accumulating.

When to use JavaScript or CSS in frontend projects is a matter of continued debate among many frontend developers. JavaScript is often the default choice for frontend development, as it offers a robust collection of libraries custom-made for creating advanced UI features, such as data-based visualizations or complex animations. But JavaScript also comes with tradeoffs, particularly when it comes to performance, accessibility, and code complexity.

This article is part of our series on how Datadog engineering teams use LLM Observability to build, monitor, and improve AI-powered systems. At Datadog, we’re always looking for ways to make complex data easier to explore.

In PostgreSQL, the EXPLAIN ANALYZE statement gives you a detailed report of what actually happens when you execute a query. This kind of information is important for troubleshooting slow queries, but using EXPLAIN ANALYZE to collect this data is often challenging in a production environment. Datadog Database Monitoring now supports automatic collection of EXPLAIN ANALYZE plans for PostgreSQL, enabling you to easily capture execution details that help you troubleshoot slow queries.

Generative AI enables teams to write and ship code faster than ever. But current methods for testing and quality assurance have not evolved to match the new pace and scale of deployments. Manual and deterministic testing paths quickly become obsolete when new features are released, and they fundamentally can’t test AI outputs, leaving a massive untested surface area. To keep up, teams need new testing methods that can define what goals users have, and ensure that their outcomes match.