Operations | Monitoring | ITSM | DevOps | Cloud

August 2019

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.

Copy and paste widgets to share data across teams and dashboards

As your environment grows in scale and complexity, finding faster ways to build rich dashboards and share strategic insights with the right team members becomes more important. To help you easily share data with anyone, anywhere, we are happy to announce that you can now copy and paste widgets within Datadog (across dashboards, Notebooks, and accounts)—and even in emails and other communication channels like Slack.

Monitor Harbor container registry with Datadog

Harbor, developed by VMware and hosted by the CNCF, is an open source registry for container images and Helm charts. Hosting Harbor within your infrastructure gives you a number of advantages over using the default Docker registry, such as role-based access control, security scanning, and replication of resources between registry instances. Since a failed Harbor deployment can spell trouble for your containerized workloads, monitoring your self-hosted container registry is critical.

A look back at Dash 2019: Two days of talks, workshops, and community

Thanks to all who attended our second annual Dash conference! We hope that you enjoyed your time with us at New York City’s Chelsea Piers, and that you were able to learn about building and scaling systems and teams in our breakout sessions and workshops. For those of you who were unable to attend, we hope to see you next year. Check out some of the highlights from our two-day conference below.

Operational Controls at the BBC

The BBC is the world's largest broadcaster, and is home to a wide range of popular services. Ensuring service availability is a key concern of the BBCs product teams, and they’ve invested in operational controls to help them achieve this. Their portfolio is comprised of thousands of services that communicate together to deliver live TV and radio, on-demand content, and a vast high-traffic website.

Serverless from Scratch

Openfit is a new fitness streaming service by Beachbody that streams hundreds of thousands of hours of video to tens of thousands of users each month with a 100% serverless architecture. From development to testing and production workloads, Reza Javidi (Director of DevOps & SRE) shares best practices his teams have developed for scaling and securing serverless workloads—both in terms of traffic and development velocity.

Building a Real Time Metrics Database at Datadog

In the course of its eight years of existence, Datadog has grown its real time metrics systems that collect, process, and visualize data to the point they now handle trillions of points per day. This has been based on an architecture combining open source technologies, such as Apache Cassandra, Kafka, and PostgreSQL, with a lot of in-house software for in-memory data storing and querying.