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.
Amazon Elastic Container Service for Kubernetes, or Amazon EKS, is a hosted Kubernetes platform that is managed by AWS. Put another way, EKS is Kubernetes-as-a-service, with AWS hosting and managing the infrastructure needed to make your cluster highly available across multiple availability zones. EKS is distinct from Amazon Elastic Container Service (ECS), which is Amazon’s proprietary container orchestration service for running and managing Docker containers.
In Part 1 of this series, we looked at key metrics for tracking the performance and health of your EKS cluster. Recall that these EKS metrics fall into three general categories: Kubernetes cluster state metrics, resource metrics (at the node and container level), and AWS service metrics. In this post, we will go over methods for accessing these categories of metrics, broken down by where they are generated.
In this post, we’ll explore how Datadog’s integrations with Kubernetes, Docker, and AWS will let you track the full range of EKS metrics, as well as logs and performance data from your cluster and applications. Datadog gives you comprehensive coverage of your dynamic infrastructure and applications with features like Autodiscovery to track services across containers; sophisticated graphing and alerting options; and full support for AWS services.
Logging your Java applications is essential to getting visibility into application performance and troubleshooting problems. But, with a variety of available libraries and configuration options, getting started logging in Java can be challenging.
