Operations | Monitoring | ITSM | DevOps | Cloud

Asynchronous communication patterns are commonly used in distributed systems, especially in those that rely on events or messages to coordinate activity. Rather than responding to direct API calls like in a traditional request-response architecture, services in an asynchronous system produce, route, or consume events and messages independently.

Mobile applications offer increased flexibility to both users and developers. Users can access content on a wide range of devices, operating systems, and network types, while developers can leverage touch screens and orientation-based layouts to create more responsive features. However, all of these factors create new testing challenges. To ensure a good user experience (UX), developers have to test their apps across many device models and platforms, which can become costly and time-consuming.

As you analyze your organization's cloud spending, you'll often find that stakeholders have different perceptions of what that spending brings you. This is especially true when overall costs are rising and it's hard to distinguish waste from valuable investments in growth. But when finance, engineering, and product teams can all connect cloud spending to specific business outcomes, you gain the ability to make data-driven decisions about how to maximize the value of that spending.

Modern infrastructure is built and deployed faster than ever, but increased speed can elevate risk. Developers who work on cloud-native applications often use infrastructure as code (IaC) to define cloud resources in configuration files, which are then shared across teams and deployed automatically. Although this approach is efficient, undetected misconfigurations in IaC can quickly introduce security risks into production environments.

"There are only two hard things in Computer Science: cache invalidation and naming things."—Phil Karlton In the age of increasingly frequent deploys, edge caching, and Jamstack adoption, caching plays a key role across the software delivery life cycle. In build and CI pipelines, caching compiled assets or dependencies helps reduce compute costs, speed up job runtimes, and lower the environmental impact (regarding energy usage) of repeated builds.

In July’s episode of This Month in Datadog, we’re doing things differently by spotlighting the people behind the products you rely on. Jeremy is joined by Tristan Ratchford to discuss saving time and effort when you’re on call with Bits AI SRE, and by Kevin Hu to explore gaining visibility into datasets across the entire data lifecycle with Data Observability.

A loss in infrastructure and applications observability can leave SRE and DevOps teams without insight into the real-time state of their production systems, causing them to temporarily pause code deployments and limit their ability to troubleshoot issues or respond to critical alerts. In modern cloud environments, where services are distributed and deeply interconnected, this lack of visibility can escalate quickly.

In Kubernetes environments, applications often communicate with the Datadog Agent to send telemetry data such as custom metrics via DogStatsD or traces through Datadog APM. How this communication takes place depends on the communication mode set on the Datadog Cluster Agent's Admission Controller. With the sockets option, communication takes place through local inter-process communication via Unix domain sockets (UDS), whereas the service and default hostip options rely on network communication.

Developers have long used profilers to diagnose performance bottlenecks and improve the efficiency of their code. But a modern version of profiling, continuous profiling, is quietly redefining what profiling is and what it can do. By running nonstop in production with very low overhead, continuous profilers give teams always-on visibility into how their code behaves in the real world.

When organizations first begin deploying workloads on Kubernetes, it's common for them to start with a permissive egress traffic policy that allows any workload to reach the internet. This approach can make it easier for teams to stay agile and to get services up and running in fast-moving environments. But as your Kubernetes footprint grows, it's important to minimize public internet access on a per-workload basis to improve your organization's security posture.