Operations | Monitoring | ITSM | DevOps | Cloud

Modern systems produce endless streams of metrics. CPU usage, request volume, cache hit rates, node counts, queue depth, the list keeps growing. With this much data, it’s easy for teams to get lost in dashboards without knowing what actually matters. That’s why DevOps and SRE teams rely on the 4 Golden Signals of System Reliability. They provide the simplest and clearest way to understand user experience and system health.

Discover the basics of cloud efficiency as well as six advanced data-driven strategies you can use to make your cloud environment more efficient. With incredibly complex cloud architecture — that may even include Kubernetes and multi-tenant infrastructure — organizations are finding it hard to measure and monitor the performance and cost of their cloud environments.

Vendor lock-in remains one of the most significant concerns when choosing a cloud platform. When your data becomes trapped in proprietary formats or services, migration costs skyrocket and your flexibility disappears. This challenge affects organizations of all sizes, from startups planning for growth to enterprises managing complex compliance requirements.

In the last post we discussed why we’re building our own Certificate Transparency (CT) search tool. There’s good background on the CT ecosystem in that post, so check it out if you haven’t. This post assumes a certain understanding of terminology covered previously. Now that we know where the CT logs live, and the different kinds of logs, we need to start reading them.

Puppet Edge Workflows, available with Puppet Enterprise Advanced, provide the orchestration tools to define multistep workflows to run against your infrastructure. This allows Puppet experts to create workflows that Ops teams can run without having deep Puppet language knowledge or the underlying infrastructure.

The hidden blockers slowing down your incident response and how to remove them before they become reliability risks. Incidents rarely go wrong because of one big failure. Most of the time, it’s a handful of small, familiar mistakes that slow teams down, muddy communication, or create confusion in the heat of the moment. Fortunately, these mistakes are predictable and fixable.

Six months of safer releases, sharper AI insights, and a unified platform experience for feature flags and experimentation.