Operations | Monitoring | ITSM | DevOps | Cloud

If you’re using Python for search, log aggregation, or analytics, you’ve probably worked with Elasticsearch. It’s fast, scalable, and fairly complex once you go beyond the basics. The official Python client gives you raw access to Elasticsearch’s REST API. But getting it to work the way you want, especially under load, can be tricky. This blog walks through practical ways to index, query, and monitor Elasticsearch from Python code, without getting lost in the docs.

60% of Gen Z won’t engage with a slow-loading website. In today’s digital economy, that’s a deal-breaker. Whether it’s a banking portal, a travel app, or an AI-powered SaaS platform, users expect performance. Instant loading, global reliability, and smooth interactivity aren’t just nice to have—they define the winners.

Networks aren’t what they used to be. While your network operations teams still have legacy equipment to manage, they’re also contending with the expanded reliance on software-defined networking (SDN), hybrid and multi-cloud architectures, private clouds, and more. These environments are anything but static. They’re sprawling, dynamic, and evolving faster than ever—which means that establishing and retaining visibility and control is more challenging than ever.

You know the feeling. It’s that familiar rhythm of playing defense, racing from one network fire to the next. The alerts pile up, users report slowdowns, and your team of brilliant engineers spends its days tracing packets instead of focusing on the future. For years, automation has been the answer. You’ve built scripts and workflows to handle repetitive tasks, which has certainly helped.

When Microsoft engineers went looking for a modern visualization platform to help track critical signals and make quicker decisions, Grafana emerged as the clear favorite. But there was just one hitch: the available authentication methods didn’t quite meet their needs.

Today marks an exciting moment for all of us at Coralogix. We’re proud to share that Gartner has named us a Visionary in the 2025 Magic Quadrant for Observability Platforms. This recognition, we believe, reflects what we’ve been building toward for years: an observability platform that delivers scale, cost-efficiency, AI-powered insights, and tangible customer success.

Let's be honest: most conversations about AI ethics sound like they're happening in a boardroom, not an ops room. But here's the thing, when you're using AI to make sense of your telemetry data, ethics isn't some abstract concept. It's the difference between insights you can trust and algorithmic noise that leads you down the wrong path. The uncomfortable reality? Your AI is only as ethical as the messiest, most biased piece of telemetry data you feed it. And if you think your data is clean, well...

One of the most common challenges in modern software engineering today is understanding how requests flow through applications. As system architectures shift to favor widely distributed, cloud-native designs, keeping track of how an application processes user actions is more difficult than ever. A single user action may trigger events processed in dozens of backend services. Traces are helping software developers today with this challenge.

We are thrilled to announce that, for the fifth consecutive year, Datadog has been named a Leader in the 2025 Gartner Magic Quadrant for Observability Platforms. We believe that this recognition reflects our continued focus on helping customers observe, secure, and act on everything that matters across their technology stack.

You're building a classification model, and your framework throws around terms like "log loss" and "cross-entropy loss." Are they the same thing? When should you use binary cross-entropy versus categorical cross-entropy? What about focal loss? This blog breaks down these loss functions with practical examples and real-world implementations.