Windows hosts make up a significant portion of the infrastructure of many organizations, and visibility into Windows systems can be a vital observability need. We’re pleased to announce that Datadog’s Live Process monitoring is now available for Windows.
Node.js is an asynchronous JavaScript runtime that is used to develop highly scalable network applications. To help provide more visibility into these dynamic environments, we’re pleased to announce that Datadog APM has officially released support for monitoring Node.js applications, which joins our existing support for Java, Ruby, Python and Go.
With anomaly detection, outlier detection, forecasting, and composite alerting, Datadog enables you to reliably alert the right people at the right time. But what happens when latency starts to increase, or error rates spike, in areas of your application where you haven’t set alerts? That’s what Watchdog is for.
Traditional logging solutions require teams to provision and pay for a daily volume of logs, which quickly becomes cost-prohibitive without some form of server-side or agent-level filtering. But filtering your logs before sending them inevitably leads to gaps in coverage, and often filters out valuable data.
Distributed tracing provides a detailed view into application performance. Each trace shows you how an individual request was executed in your app: which user did what, which services were involved, how long it took, and whether the request executed successfully. Capturing that level of detail across hundreds or thousands of services provides a vast trove of information for troubleshooting and performance optimization, but it’s not always easy to find the exact trace events you need.
We’re happy to announce that our Go tracer v1.0.0 has been released. The latest version represents a major overhaul, and includes performance improvements, more robust compatibility with tracing standards, and a new and improved API. It incorporates continuous feedback not only from our community, but also from extensive internal usage here at Datadog.
Flask is a Python framework known for its ease of use. It inherits Python’s advantages of extensibility, broad support, and relative simplicity. It’s known as a microframework because it relies on extensions for much of its functionality. Flask avoids constraining the developer to a predetermined database or authentication mechanism, for example, and instead leaves room for choice.
Microsoft recently released Azure Kubernetes Service (AKS), a managed service that helps you deploy, run, and scale Kubernetes clusters. We’re pleased to share that Datadog’s integrations with Kubernetes and Azure Monitor can give you comprehensive visibility into your AKS infrastructure with no additional configuration.
In this post, we’ll show you how to use Datadog to collect metrics, request traces, and logs from a Rails application running on Passenger, Apache, and MySQL. (Though we will focus on these integrations in this guide, you can also monitor NGINX servers and PostgreSQL database systems with Datadog.)
We are proud to announce that we have enhanced our Microsoft Azure integration to support more than 60 Azure services, including Cosmos DB, Service Bus, and Azure DB for MySQL and PostgreSQL. Datadog now automatically collects metrics and tags from all services supported by Azure Monitor to provide comprehensive Azure monitoring through one integration.
