Reducing noise in your error logs is critical for quickly identifying bugs in your code and determining which to prioritize for remediation. To help you spot and investigate the issues causing error logs in your environments, we’re pleased to announce that Datadog Error Tracking is now available for Log Management in open beta.

AWS Lambda enables engineering teams to build modern, scalable services without the need to provision underlying infrastructure resources. But monitoring Lambda functions requires visibility into performance indicators that differ from those of traditional architectures—and cold starts are a key example.

A well-designed cloud architecture is essential to ensure that the underlying infrastructure stays operational, within budget, and compliant over time. These days, organizations are rapidly spreading their infrastructure across a broad, complex mesh of interconnected resources and services. It can be difficult to make high-level decisions about the design and management of these systems. This is why many organizations are now turning to cloud infrastructure modeling tools.

React Native has become the predominant development framework for cross-platform mobile applications. By interacting with native APIs largely under the hood and requiring only a fractional proportion of platform-specific code, it allows you to build applications for iOS, Android, and the browser using the same declarative JavaScript. But this cross-platform adaptability has its downsides.

Datadog Real User Monitoring (RUM) provides end-to-end visibility into the user experience and performance of your browser and mobile applications. RUM allows you to capture and retain complete user sessions for 30 days. This means you can pinpoint bugs, prioritize issues, and determine fixes with data collected across an entire quarter.

Properly testing a service’s APIs to ensure that it can handle production traffic presents many challenges for engineers—SREs need to guarantee the resiliency of their application, while developers must ensure that their features perform well at any given scale. Speedscale is a testing framework built for Kubernetes applications that enables you to load test with real-world production scenarios by replaying actual API traffic that your application has experienced.

In 2021, we partnered with AWS to develop the Datadog Lambda extension which provides a simple, cost-effective way for teams to collect traces, logs, custom metrics, and enhanced metrics from Lambda functions and submit them to Datadog.

Monitoring HTTP sessions offers a potentially powerful way to gain visibility into your web servers, but in practice, doing so can be complex and resource-intensive. Extended Berkeley Packet Filter (eBPF) technology allows you to overcome these challenges, giving you a simple and efficient way to process application-layer traffic for your troubleshooting needs.

Google Kubernetes Engine (GKE) is a managed Kubernetes service that enables users to deploy and orchestrate containerized applications on Google’s infrastructure. Datadog’s GKE integration, when paired with our Kubernetes integration, has always provided deep visibility into the health and performance of your clusters at the node, pod, container, and application levels.

This post is part 1 of a 3-part series about monitoring MongoDB performance with the WiredTiger storage engine. Part 2 explains the different ways to collect MongoDB metrics, and Part 3 details how to monitor its performance with Datadog. If you are using the MMAPv1 storage engine, visit the companion article “Monitoring MongoDB performance metrics (MMAP)”.