Operations | Monitoring | ITSM | DevOps | Cloud

Modern DevOps observability tools are excellent for monitoring system health, tracking distributed traces, and aggregating metrics. However, they lack the fidelity needed for full-text search across application traffic. While observability platforms excel at showing what happened and when, they often fall short when you need to find where a specific piece of data (like an email address, user ID, or transaction token) appears as it flows through your entire application stack.

API mocking plays a crucial role in modern software development allowing developers to simulate external API endpoints. It’s an effective way to isolate your application for testing and ensure that code changes don’t inadvertently break critical dependencies. Essentially, API mocking helps you create robust, reliable software by allowing you to test how your application interacts with external services.

Today, we are pleased to announce the release of CFEngine 3.27.0! The code word for this release is exploration. This release also marks an important event, the beginning of the 3.27 LTS series, which will be supported for 3 years. Several new features have been added since the release of CFEngine 3.24 LTS, in the form of non-LTS releases.

The open-source Kubernetes platform is designed to help simplify application deployment through Linux containers. It supports tasks like deploying workloads in the form of pods, clustering nodes, managing container runtimes, and tracking resources. The Kubernetes microservices system has risen in popularity over the last several years as an easy way to support, scale, and manage applications.

Heroku makes it easy to deploy and operate applications without managing servers, but understanding how your application behaves internally still requires instrumentation. Platform metrics like CPU usage, memory consumption, and router request/status counts are useful, but they don’t tell you how long your code takes to run, when your app throws errors, or whether users are interacting with key features.

This guide covers battle-tested chaos experiments for Kubernetes, AWS, Azure, and GCP to help you validate production resilience before real failures happen. Start with low blast radius experiments (pod-level) and gradually progress to higher impact scenarios (node/zone failures), always defining clear hypotheses and using probes to measure results. Building reliable distributed systems isn't just about writing good code. It's about understanding how your systems behave when things go wrong.

Damaging cyber attacks are a rising concern as organizations increasingly rely on digital technology for managing sensitive data and running core business operations. While technology can increase business efficiency, without security measures in place, a digital-first approach can end up introducing vulnerabilities and putting data at risk.

The way Kubernetes works “under the hood” dictates which components are more important for improving performance and which are less important. So let’s talk about the Kubernetes internals first.

When you're on a software team, planning for the weeks and months to come is always a challenge. You have to balance deep feature backlogs, business and leadership requests, customer requests, and operational interruptions. Effective planning requires a way to prioritize the backlog, set realistic roadmap goals, and justify decisions.