Operations | Monitoring | ITSM | DevOps | Cloud

Remember when monitoring your apps meant checking if they were up or down? Yeah, those days are long gone. As systems have gotten more complex—microservices talking to other microservices, containers spinning up and down, serverless functions doing their thing—the approach to understanding system health has had to level up too. APM tools have been the bread and butter for DevOps teams for years, but now everyone's talking about observability.

Let's talk about something central to DevOps work: logging vs monitoring. While both are essential components of maintaining system health and reliability, they serve distinct purposes and complement each other in different ways. The distinction between them isn't always clear-cut, especially as tooling continues to evolve. This guide talks about the practical applications, technical differences, and implementation strategies for both logging and monitoring in modern DevOps environments.

Effective database monitoring is essential for maintaining application performance and reliability. This guide explores how to implement OpenTelemetry with PostgreSQL, providing DevOps engineers and Site Reliability Engineers with practical steps for comprehensive database observability.

Amazon EventBridge is a powerful tool for building event-driven architectures. It’s built to connect and simplify real-time data processing and automation across your cloud stack (AWS services, customs apps, and SaaS applications). But when it comes to pricing, things can get a little tricky. Estimating EventBridge costs at scale is often daunting. There are just too many moving parts involved. That’s why in this guide, we’re breaking down what goes into an Amazon EventBridge bill.

Explore how ephemeral environments help development teams scale efficiently. This strategic guide for CTOs covers key benefits, technical insights, and practical steps to boost deployment speed, reduce costs, and streamline testing.

Modern code profilers gather performance data that is highly useful for developers, but the traditional presentation of that data can be challenging to interpret for engineers who are new to profiling. For the Continuous Profiler team at Datadog, our guiding mission is to make profiling a standard practice for all developers by flattening its learning curve and helping teams quickly gain insights into application performance.