Operations | Monitoring | ITSM | DevOps | Cloud

In my previous blog post, I explained how to build lists using ipl-web widgets. That method will soon be deprecated due to its complexity. With the recent ipl-web release, we have introduced a simpler and more flexible approach to building lists, using a lightweight rendering interface and a single class, as described below.

In modern observability stacks, log collection is a critical component. Among the most widely adopted logs collector are Fluentd and Logstash. Both tools are designed to collect, process, and forward logs to various destinations like Elasticsearch, Kafka, and cloud services. However, the differences between FluentD and Logstash lie significantly in their design, performance, plugin ecosystems, and user experiences.

GPUs are everywhere powering LLM inference, model training, video processing, and more. Kubernetes is often where these workloads run. But using GPUs in Kubernetes isn’t as simple as using CPUs. You need the right setup. You need efficient scheduling. And most importantly you need visibility. This post walks through how to run GPU workloads on Kubernetes, how to virtualize them efficiently, and how Coroot helps you monitor everything with zero instrumentation or config.

Datadog has long supported the monitoring of instrumented Go applications through our Go tracer v1. As the Go ecosystem has continued to mature, we’ve been hard at work collecting feedback and improving upon the tracer’s capabilities and usability features. We are now thrilled to announce the release of our Go tracer v2.0.0. This major update includes better security and stability, and a new and simplified API.

We’ve noticed a lot of our customers spend countless hours building and configuring processors. Either parsing JSON, standardizing log formats, normalizing timestamps, masking PII, de-duplicating logs, the list never ends. Most work revolves around recreating the same processor bundles in multiple processor nodes. Bindplane’s new Blueprints solves that boring, repetitive work by providing pre-built processor bundles you can drop into any pipeline with a single click.

When you run critical IT or Security systems, you are still on the hook if an alert is missed or a pipeline drops data. That is why Cribl Copilot never tries to replace you. Instead, it works as a partner you can question, guide, and overrule.

I’m genuinely excited about what we're rolling out with Copilot Editor, an update to our AI that’s truly packed with new capabilities designed to help you automate pipeline development. You can read about these capabilities here. I wanted to take a moment to share our thinking on a core principle that guides how we build, especially regarding the impactful, and sometimes daunting, world of generative AI.

Prometheus can be lightweight to start with, but once it’s in production, storage usage tends to grow faster than expected. Managing how long data is kept becomes critical, especially when you're working with limited disk space or tight budgets. This guide outlines the key concepts behind Prometheus data retention, how to configure it effectively, and what to watch out for.