Service level objectives are an important tool for maintaining application performance, ensuring a consistent customer experience, and setting expectations about service performance for both internal and external users. We are very pleased to announce the availability of a new monitor uptime and SLO widget that makes it simple to monitor the status of your SLOs and communicate that status to your teams, executives, or external customers.
Datadog’s new automated browser tests enable you to automate your user experience monitoring and ensure that your users can complete actions like signing up for a new account or adding items to a cart. Anyone on your team can record and automate multistep browser tests in minutes. Once you create a test, Datadog uses machine learning to detect changes to your application and automatically update your tests accordingly.
When your users are encountering errors or high latency in your application, drilling down to view the logs from a problematic request can reveal exactly what went wrong. By pulling together all the logs pertaining to a given request, you can see in rich detail how it was handled from beginning to end so you can quickly diagnose the issue.
Even in an era where container, serverless, and cloud-computing technologies garner considerable attention, many companies continue to run a sizeable share of their mission-critical applications on highly resilient, fault-tolerant systems such as IBM AIX on Power-series hardware. AIX, one of the most popular Unix-based operating systems, is trusted by large companies that process critical data such as medical health records and banking transactions.
Python’s built-in logging module is designed to give you critical visibility into your applications with minimal setup. Whether you’re just getting started or already using Python’s logging module, this guide will show you how to configure this module to log all the data you need, route it to your desired destinations, and centralize your logs to get deeper insights into your Python applications.
Google Cloud’s Stackdriver Logging is a managed service that centralizes and stores logs from your Google Cloud Platform services and applications. We are excited to announce that Datadog’s GCP integration now includes Stackdriver Logging. You can collect all your GCP logs using Datadog so you can search, filter, analyze, and alert on them along with your metrics and distributed request traces in a single platform.
Google Cloud Run is a new compute platform for running stateless containers without having to manage the underlying infrastructure. You can choose between a fully managed version of Cloud Run, or run container workloads in your existing Google Kubernetes Engine (GKE) clusters with Cloud Run on GKE.
In an outage, every minute counts—and real-time communication is essential for helping teams collaborate to reduce mean time to resolution. If you’re using Google Hangouts Chat as your communication platform, Datadog’s new integration allows your team to share and discuss annotated graphs, see when alerts are triggered, and instantly start collaborating to resolve issues.
Since it was first introduced in 2002, Microsoft’s .NET Framework has garnered a robust user base that includes organizations like UPS, Stack Overflow, and Jet.com. And now, thanks to the rise of the .NET Core runtime, this high-performance framework also supports cross-platform development. To provide deeper visibility into all of these environments, we are pleased to announce that Datadog APM and distributed tracing are generally available for .NET Framework and .NET Core applications.
