It’s considered common practice for platform engineering teams to build their IDP to be a self-service product for developers. This means, among other things, building a dedicated team around it for upkeep and support, creating a roadmap for development, and coming up with metrics they can use to determine its success.

Managing logs in Docker can quickly become overwhelming, especially when dealing with multiple containers. If you’ve ever tried to sift through a sea of log entries looking for a specific error or debugging message, you know the struggle. Fortunately, you can pipe docker logs output through grep to filter logs efficiently. This guide breaks down how to use docker logs grep it effectively, including practical examples to help you debug and monitor your containerized applications like a pro.

Efficient resource utilization is key to running Kubernetes workloads smoothly. Whether you're troubleshooting performance issues, optimizing resource requests and limits, or keeping an eye on cluster health, the kubectl top command is an essential tool. It provides real-time CPU and memory usage metrics for nodes and pods, helping you make informed decisions about scaling and resource allocation.

When it comes to infusing artificial intelligence (AI) into enterprise applications, developers, platform engineers and data scientists are facing a tremendous opportunity. However, in Tanzu, we are also hearing about their struggles to get to production, not to mention achieving positive return on investment (ROI).

Amazon Web Services (AWS) offers over 200 fully-featured services. AWS Elastic Compute Cloud (EC2), Elastic Container Service (ECS), Amazon Lambda, and the AWS Simple Storage Service (Amazon S3) are some of the most critical services you should become familiar with. We’ve previously covered Amazon ECS vs. EKS vs. Fargate for managing and deploying containers. This guide will explain how Amazon EC2, Lambda, ECS, and S3 compare and when you’ll want to use each.

Kubernetes is the standard for orchestrating containerized microservices — but it can present some monitoring challenges. Luckily, we’ve already covered why monitoring Kubernetes is a must-do, the basics of how to do it, and the options you have for collecting monitoring data from a K8s environment.

Have you ever wondered how to navigate the complexities of managing Kubernetes clusters effectively? Observability is the key, and Elasticsearch plays a pivotal role in storing and analyzing the critical data that keeps your systems running smoothly.

Following all the hype and bluster with DeepSeek’s arrival in the AI landscape––and its ability to crash the poster child of AI’s share value overnight (Nvidia), we wanted to conduct a rigorous evaluation at Komodor. We tested DeepSeek’s models head-to-head against industry leaders in solving real-world Kubernetes challenges.

There are several big barriers to an effective tracing strategy. Modern applications require complex code instrumentation, and legacy applications might not be so easy to alter, and that’s assuming every engineering team can be engaged to make the necessary changes. eBPF & OpenTelemetry flip this entire problem on its head, and Coralogix is one of the first major observability platforms to leverage this exciting functionality, to provide an unobtrusive, low risk overview of your system.

When you get behind the wheel of your car, one of the first things you see is the dashboard. Your dashboard provides various information about all the different technologies that make the car run smoothly, like helping you control your speed, providing insight into your fuel levels, and offering suggestions for regular maintenance, like oil changes. For developers, Kubernetes acts as that one-glance dashboard to provide insights about container performance, maintenance needs, and storage requirements.