Operations | Monitoring | ITSM | DevOps | Cloud

How to collect and manage all of your multi-line logs

Multi-line logs such as stack traces give you lots of very valuable information for debugging and troubleshooting application problems. But, as anyone who has tried knows, it can be a challenge to collect stack traces and other multi-line logs so that you can easily parse, search, and use them to identify problems. This is because, without proper configuration, log management services and tools do not treat multi-line logs as a single event.

Monitor system access and unusual activity with Okta logs and Datadog

Okta is a cloud-based identity management service that provides authentication and authorization tools for your organizations’ employees and users. You can use Okta to incorporate single sign-on, multi-factor authentication, and user management services right into your applications.

Troubleshoot .NET apps with auto-correlated traces and logs

Collecting observability data like metrics, traces, and logs makes it much easier to identify bottlenecks and other performance problems in your .NET applications. When you need to troubleshoot a production incident, it’s especially important to be able to navigate all that data so you can find the source of the issue and enact a timely resolution.

How to monitor Google Kubernetes Engine with Datadog

Google Kubernetes Engine (GKE), a service on the Google Cloud Platform (GCP), is a hosted platform for running and orchestrating containerized applications. Similar to Amazon’s Elastic Container Service (ECS), GKE manages Docker containers deployed on a cluster of machines. However, unlike ECS, GKE uses Kubernetes, an increasingly popular open source orchestrator that can deploy, schedule, and scale containers on the fly.

Integrate Akamai mPulse real user monitoring with Datadog

Akamai mPulse is a real user monitoring (RUM) service that enables organizations to get deep visibility into end user experience across their websites or applications. With mPulse, businesses can collect high-granularity metrics directly from their users’ browsers, and then analyze that data to pinpoint slow resources (e.g., third-party scripts), track user engagement, and make decisions to improve the performance of their products.